Barandela, R.,
Sánchez, J.S.,
García, V.,
Rangel, E.,
Strategies for learning in class imbalance problems,
PR(36), No. 3, March 2003, pp. 849-851.
Elsevier DOI
0301
BibRef
Sun, Y.M.[Yan-Min],
Kamel, M.S.[Mohamed S.],
Wong, A.K.C.[Andrew K.C.],
Wang, Y.[Yang],
Cost-sensitive boosting for classification of imbalanced data,
PR(40), No. 12, December 2007, pp. 3358-3378.
Elsevier DOI
0709
Classification; Class imbalance problem; AdaBoost; Cost-sensitive learning
BibRef
Tang, Y.,
Zhang, Y.Q.,
Chawla, N.V.,
Krasser, S.,
SVMs Modeling for Highly Imbalanced Classification,
SMC-B(39), No. 1, February 2009, pp. 281-288.
IEEE DOI
0902
BibRef
Perez-Godoy, M.D.[Maria Dolores],
Fernandez, A.[Alberto],
Rivera, A.J.[Antonio Jesus],
Jose del Jesus, M.[Maria],
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for
imbalanced data sets,
PRL(31), No. 15, 1 November 2010, pp. 2375-2388.
Elsevier DOI
1003
Neural networks; Radial-basis function networks; Genetic algorithm;
Imbalanced data sets; SMOTE pre-processing method
BibRef
Fernandez-Navarro, F.[Francisco],
Hervas-Martinez, C.[Cesar],
Gutierrez, P.A.[Pedro Antonio],
A dynamic over-sampling procedure based on sensitivity for multi-class
problems,
PR(44), No. 8, August 2011, pp. 1821-1833.
Elsevier DOI
1104
Classification; Multi-class; Sensitivity; Accuracy; Memetic algorithm;
Imbalanced datasets; Over-sampling method; SMOTE
BibRef
Soda, P.[Paolo],
A multi-objective optimisation approach for class imbalance learning,
PR(44), No. 8, August 2011, pp. 1801-1810.
Elsevier DOI
1104
Pattern recognition; Machine learning; Class imbalance learning;
Multi-objective optimisation
BibRef
Tahir, M.A.[Muhammad Atif],
Kittler, J.V.[Josef V.],
Bouridane, A.[Ahmed],
Multilabel classification using heterogeneous ensemble of multi-label
classifiers,
PRL(33), No. 5, 1 April 2012, pp. 513-523.
Elsevier DOI
1202
Multilabel classification; Heterogeneous ensemble of multilabel
classifiers; Static/dynamic weighting
BibRef
Tahir, M.A.[Muhammad Atif],
Kittler, J.V.[Josef V.],
Yan, F.[Fei],
Inverse random under sampling for class imbalance problem and its
application to multi-label classification,
PR(45), No. 10, October 2012, pp. 3738-3750.
Elsevier DOI
1206
Class imbalance problem; Multi-label classification; Inverse random
under sampling
BibRef
Thanathamathee, P.[Putthiporn],
Lursinsap, C.[Chidchanok],
Handling imbalanced data sets with synthetic boundary data generation
using bootstrap re-sampling and AdaBoost techniques,
PRL(34), No. 12, 1 September 2013, pp. 1339-1347.
Elsevier DOI
1306
Imbalanced data; Boundary data; Synthetic data generation;
Bootstrap re-sampling; AdaBoost
BibRef
Galar, M.[Mikel],
Fernández, A.[Alberto],
Barrenechea, E.[Edurne],
Herrera, F.[Francisco],
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by
evolutionary undersampling,
PR(46), No. 12, 2013, pp. 3460-3471.
Elsevier DOI
1308
Classification
See also Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers.
BibRef
Maldonado, S.[Sebastián],
López, J.[Julio],
Imbalanced data classification using second-order cone programming
support vector machines,
PR(47), No. 5, 2014, pp. 2070-2079.
Elsevier DOI
1402
Class-imbalanced data
BibRef
Shao, Y.H.[Yuan-Hai],
Chen, W.J.[Wei-Jie],
Zhang, J.J.[Jing-Jing],
Wang, Z.[Zhen],
Deng, N.Y.[Nai-Yang],
An efficient weighted Lagrangian twin support vector machine for
imbalanced data classification,
PR(47), No. 9, 2014, pp. 3158-3167.
Elsevier DOI
1406
Imbalanced data classification
BibRef
Sun, Z.B.[Zhong-Bin],
Song, Q.B.[Qin-Bao],
Zhu, X.Y.[Xiao-Yan],
Sun, H.[Heli],
Xu, B.[Baowen],
Zhou, Y.M.[Yu-Ming],
A novel ensemble method for classifying imbalanced data,
PR(48), No. 5, 2015, pp. 1623-1637.
Elsevier DOI
1502
Imbalanced data
BibRef
d'Addabbo, A.[Annarita],
Maglietta, R.[Rosalia],
Parallel selective sampling method for imbalanced and large data
classification,
PRL(62), No. 1, 2015, pp. 61-67.
Elsevier DOI
1507
Imbalanced learning
BibRef
Cheng, F.Y.[Fan-Yong],
Zhang, J.[Jing],
Wen, C.H.[Cui-Hong],
Cost-Sensitive Large margin Distribution Machine for classification
of imbalanced data,
PRL(80), No. 1, 2016, pp. 107-112.
Elsevier DOI
1609
Minimum margin
BibRef
Zhang, X.G.[Xiao-Gang],
Wang, D.X.[Ding-Xiang],
Zhou, Y.C.[Yi-Cong],
Chen, H.[Hua],
Cheng, F.Y.[Fan-Yong],
Liu, M.[Min],
Kernel modified optimal margin distribution machine for imbalanced
data classification,
PRL(125), 2019, pp. 325-332.
Elsevier DOI
1909
Margin distribution, Imbalanced data classification,
Kernel modification, Balanced detection rate, Generalization performance
BibRef
Sáez, J.A.[José A.],
Krawczyk, B.[Bartosz],
Wozniak, M.[Michal],
Analyzing the oversampling of different classes and types of examples
in multi-class imbalanced datasets,
PR(57), No. 1, 2016, pp. 164-178.
Elsevier DOI
1605
Machine learning
BibRef
Vluymans, S.[Sarah],
Tarragó, D.S.[Dánel Sánchez],
Saeys, Y.[Yvan],
Cornelis, C.[Chris],
Herrera, F.[Francisco],
Fuzzy rough classifiers for class imbalanced multi-instance data,
PR(53), No. 1, 2016, pp. 36-45.
Elsevier DOI
1602
Multi-instance learning
BibRef
Ng, W.W.Y.[Wing W.Y.],
Zeng, G.J.[Guang-Jun],
Zhang, J.J.[Jiang-Jun],
Yeung, D.S.[Daniel S.],
Pedrycz, W.[Witold],
Dual autoencoders features for imbalance classification problem,
PR(60), No. 1, 2016, pp. 875-889.
Elsevier DOI
1609
Imbalanced Classification
BibRef
Zhang, X.Z.[Xiu-Zhen],
Li, Y.X.[Yu-Xuan],
Kotagiri, R.[Ramamohanarao],
Wu, L.F.[Li-Fang],
Tari, Z.[Zahir],
Cheriet, M.[Mohamed],
KRNN: k Rare-class Nearest Neighbour classification,
PR(62), No. 1, 2017, pp. 33-44.
Elsevier DOI
1705
Imbalanced classification
BibRef
Zhu, C.M.[Chang-Ming],
Wang, Z.[Zhe],
Entropy-based matrix learning machine for imbalanced data sets,
PRL(88), No. 1, 2017, pp. 72-80.
Elsevier DOI
1703
Entropy
BibRef
Xu, Y.,
Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced
Data Classification,
Cyber(47), No. 6, June 2017, pp. 1540-1550.
IEEE DOI
1706
Computational efficiency, Cybernetics, Kernel, Linear programming,
Minimization, Quadratic programming, Support vector machines,
Homocentric sphere, imbalanced data classification,
maximum margin,
maximum margin of twin spheres support vector machine (MMTSSVM),
twin, support, vector, machine, (TSVM)
BibRef
Gónzalez, S.[Sergio],
García, S.[Salvador],
Lázaro, M.[Marcelino],
Figueiras-Vidal, A.R.[Aníbal R.],
Herrera, F.[Francisco],
Class Switching according to Nearest Enemy Distance for learning from
highly imbalanced data-sets,
PR(70), No. 1, 2017, pp. 12-24.
Elsevier DOI
1706
Imbalanced classification
BibRef
Devi, D.[Debashree],
Biswas, S.K.[Saroj K.],
Purkayastha, B.[Biswajit],
Redundancy-driven modified Tomek-link based undersampling:
A solution to class imbalance,
PRL(93), No. 1, 2017, pp. 3-12.
Elsevier DOI
1706
Data, mining
BibRef
Tang, B.[Bo],
He, H.B.[Hai-Bo],
GIR-based ensemble sampling approaches for imbalanced learning,
PR(71), No. 1, 2017, pp. 306-319.
Elsevier DOI
1707
Imbalanced, learning
BibRef
Zhu, T.F.[Tuan-Fei],
Lin, Y.P.[Ya-Ping],
Liu, Y.[Yonghe],
Synthetic minority oversampling technique for multiclass imbalance
problems,
PR(72), No. 1, 2017, pp. 327-340.
Elsevier DOI
1708
Multiclass imbalance problems
BibRef
Ortigosa-Hernández, J.[Jonathan],
Inza, I.[Iñaki],
Lozano, J.A.[Jose A.],
Measuring the class-imbalance extent of multi-class problems,
PRL(98), No. 1, 2017, pp. 32-38.
Elsevier DOI
1710
Class-imbalance, problem
BibRef
Kang, Q.,
Chen, X.,
Li, S.,
Zhou, M.,
A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification,
Cyber(47), No. 12, December 2017, pp. 4263-4274.
IEEE DOI
1712
Approximation algorithms, Benchmark testing, Computers,
Cybernetics, Data preprocessing, Noise measurement, Training,
under-sampling
BibRef
Castellanos, F.J.[Francisco J.],
Valero-Mas, J.J.[Jose J.],
Calvo-Zaragoza, J.[Jorge],
Rico-Juan, J.R.[Juan R.],
Oversampling imbalanced data in the string space,
PRL(103), 2018, pp. 32-38.
Elsevier DOI
1802
Class imbalance problem, Oversampling, String space, SMOTE
BibRef
Yuan, X.H.[Xiao-Hui],
Xie, L.J.[Li-Jun],
Abouelenien, M.[Mohamed],
A regularized ensemble framework of deep learning for cancer
detection from multi-class, imbalanced training data,
PR(77), 2018, pp. 160-172.
Elsevier DOI
1802
Ensemble, Deep learning, Imbalanced data, Cancer detection
BibRef
Krawczyk, B.[Bartosz],
McInnes, B.T.[Bridget T.],
Local ensemble learning from imbalanced and noisy data for word sense
disambiguation,
PR(78), 2018, pp. 103-119.
Elsevier DOI
1804
Machine learning, Natural language processing,
Imbalanced classification, Multi-class imbalance,
Word sense disambiguation
BibRef
Fernández-Baldera, A.[Antonio],
Buenaposada, J.M.[José M.],
Baumela, L.[Luis],
BAdaCost: Multi-class Boosting with Costs,
PR(79), 2018, pp. 467-479.
Elsevier DOI
1804
BibRef
Earlier:
Multi-class Boosting for Imbalanced Data,
IbPRIA15(57-64).
Springer DOI
1506
Boosting, Multi-class classification,
Cost-sensitive classification, Multi-view object detection
BibRef
Li, S.[Shuai],
Song, W.F.[Wen-Feng],
Qin, H.[Hong],
Hao, A.[Aimin],
Deep variance network: An iterative, improved CNN framework for
unbalanced training datasets,
PR(81), 2018, pp. 294-308.
Elsevier DOI
1806
Deep variance network, Unbalanced training datasets,
Convolutional neural network, Homogeneity, Heterogeneity
BibRef
Das, S.[Swagatam],
Datta, S.[Shounak],
Chaudhuri, B.B.[Bidyut B.],
Handling data irregularities in classification:
Foundations, trends, and future challenges,
PR(81), 2018, pp. 674-693.
Elsevier DOI
1806
Data irregularities, Class imbalance, Small disjuncts,
Class-distribution skew, Missing features, Absent features
BibRef
Metzler, G.[Guillaume],
Badiche, X.[Xavier],
Belkasmi, B.[Brahim],
Fromont, E.[Elisa],
Habrard, A.[Amaury],
Sebban, M.[Marc],
Learning maximum excluding ellipsoids from imbalanced data with
theoretical guarantees,
PRL(112), 2018, pp. 310-316.
Elsevier DOI
1809
Imbalanced data, Classification, Metric learning,
Statistical machine learning, Uniform stability,
Support vector data description
BibRef
Gautheron, L.[Leo],
Habrard, A.[Amaury],
Morvant, E.[Emilie],
Sebban, M.[Marc],
Metric Learning from Imbalanced Data with Generalization Guarantees,
PRL(133), 2020, pp. 298-304.
Elsevier DOI
2005
Imbalanced Data, Classification, Metric Learning,
Statistical Machine Learning, Uniform Stability
BibRef
Zhu, R.[Rui],
Wang, Z.Y.[Zi-Yu],
Ma, Z.Y.[Zhan-Yu],
Wang, G.J.[Gui-Jin],
Xue, J.H.[Jing-Hao],
LRID: A new metric of multi-class imbalance degree based on
likelihood-ratio test,
PRL(116), 2018, pp. 36-42.
Elsevier DOI
1812
Imbalanced learning, Imbalance degree, Likelihood ratio, Class distribution
BibRef
Luque, A.[Amalia],
Carrasco, A.[Alejandro],
Martín, A.[Alejandro],
de las Heras, A.[Ana],
The impact of class imbalance in classification performance metrics
based on the binary confusion matrix,
PR(91), 2019, pp. 216-231.
Elsevier DOI
1904
Classification, Performance measures, Imbalanced datasets, Class Balance Metrics
BibRef
Zhou, G.J.[Guang-Jiao],
Zhang, Y.[Ye],
Transfer and Association:
A Novel Detection Method for Targets without Prior Homogeneous Samples,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link
1907
Unbalanced data.
BibRef
Sun, F.[Fei],
Wang, R.[Run],
Wan, B.[Bo],
Su, Y.J.[Yan-Jun],
Guo, Q.H.[Qing-Hua],
Huang, Y.X.[You-Xin],
Wu, X.C.[Xin-Cai],
Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover
Classification Using an Extended Margin and Disagreement Performance,
IJGI(8), No. 7, 2019, pp. xx-yy.
DOI Link
1908
BibRef
Sadhukhan, P.[Payel],
Palit, S.[Sarbani],
Reverse-nearest neighborhood based oversampling for imbalanced,
multi-label datasets,
PRL(125), 2019, pp. 813-820.
Elsevier DOI
1909
Reverse nearest neighborhood, Multi-label classification,
Multi-label learning, Class-imbalance, Oversampling
BibRef
Indraswari, R.[Rarasmaya],
Kurita, T.[Takio],
Arifin, A.Z.[Agus Zainal],
Suciati, N.[Nanik],
Astuti, E.R.[Eha Renwi],
Multi-projection deep learning network for segmentation of 3D medical
images,
PRL(125), 2019, pp. 791-797.
Elsevier DOI
1909
Deep learning, Image segmentation, Imbalanced dataset,
Neural networks, Three-dimensional medical image
BibRef
Kim, Y.G.[Young-Geun],
Kwon, Y.C.[Yong-Chan],
Paik, M.C.[Myunghee Cho],
Valid oversampling schemes to handle imbalance,
PRL(125), 2019, pp. 661-667.
Elsevier DOI
1909
Imbalance, Oversampling,
Optimal oversampling target proportion, Resampling at random, Medical imaging
BibRef
Kaur, H.[Harsurinder],
Pannu, H.S.[Husanbir Singh],
Malhi, A.K.[Avleen Kaur],
A Systematic Review on Imbalanced Data Challenges in Machine Learning:
Applications and Solutions,
Surveys(52), No. 4, September 2019, pp. Article No 79.
DOI Link
1912
Survey, Imbalanced Data.
BibRef
Douzas, G.[Georgios],
Bacao, F.[Fernando],
Fonseca, J.[Joao],
Khudinyan, M.[Manvel],
Imbalanced Learning in Land Cover Classification: Improving Minority
Classes' Prediction Accuracy Using the Geometric SMOTE Algorithm,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link
1912
BibRef
Koziarski, M.[Michal],
Radial-Based Undersampling for imbalanced data classification,
PR(102), 2020, pp. 107262.
Elsevier DOI
2003
Machine learning, Classification, Imbalanced data,
Undersampling, Radial basis functions
BibRef
Mullick, S.S.[Sankha Subhra],
Datta, S.[Shounak],
Dhekane, S.G.[Sourish Gunesh],
Das, S.[Swagatam],
Appropriateness of performance indices for imbalanced data
classification: An analysis,
PR(102), 2020, pp. 107197.
Elsevier DOI
2003
Imbalanced classification, Performance evaluation indices,
Precision, Recall, GMean, Area under the curve
BibRef
Richhariya, B.,
Tanveer, M.,
A reduced universum twin support vector machine for class imbalance
learning,
PR(102), 2020, pp. 107150.
Elsevier DOI
2003
Universum, Rectangular kernel, Class imbalance, Imbalance ratio,
Twin support vector machine
BibRef
Shi, C.H.[Cang-Hong],
Li, X.J.[Xiao-Jie],
Lv, J.C.[Jian-Cheng],
Yin, J.[Jing],
Mumtaz, I.[Imran],
Robust geodesic based outlier detection for class imbalance problem,
PRL(131), 2020, pp. 428-434.
Elsevier DOI
2004
Outlier detection, Structural stability, Local structure
BibRef
Zhu, R.[Rui],
Guo, Y.W.[Yi-Wen],
Xue, J.H.[Jing-Hao],
Adjusting the imbalance ratio by the dimensionality of imbalanced
data,
PRL(133), 2020, pp. 217-223.
Elsevier DOI
2005
Imbalanced data, Imbalance extent, Imbalanced learning,
Imbalance ratio, Pearson correlation test
BibRef
Huang, C.X.[Chen-Xi],
Huang, X.[Xin],
Fang, Y.[Yu],
Xu, J.F.[Jian-Feng],
Qu, Y.[Yi],
Zhai, P.J.[Peng-Jun],
Fan, L.[Lin],
Yin, H.[Hua],
Xu, Y.[Yilu],
Li, J.H.[Jia-Hang],
Sample imbalance disease classification model based on association
rule feature selection,
PRL(133), 2020, pp. 280-286.
Elsevier DOI
2005
Association rules, Feature selection, Integrated learning, Sample imbalance
BibRef
Xiao, G.B.[Guo-Bao],
Zhou, X.[Xiong],
Yan, Y.[Yan],
Wang, H.Z.[Han-Zi],
A two-step hypergraph reduction based fitting method for unbalanced
data,
PRL(134), 2020, pp. 106-115.
Elsevier DOI
2005
Hypergraph reduction, Hypergraph construction, Unbalanced data, Model fitting
BibRef
Jimenez-Castaño, C.,
Alvarez-Meza, A.,
Orozco-Gutierrez, A.,
Enhanced automatic twin support vector machine for imbalanced data
classification,
PR(107), 2020, pp. 107442.
Elsevier DOI
2008
Imbalanced data, Kernel methods, Twin support vector machines
BibRef
Jimenez-Castaño, C.,
Álvarez-Meza, A.,
Cárdenas-Peña, D.,
Orozco-Gutíerrez, A.,
Guerrero-Erazo, J.,
Krein twin support vector machines for imbalanced data classification,
PRL(182), 2024, pp. 39-45.
Elsevier DOI
2405
Imbalanced classification, Krein spaces, Kernel methods, Support vector machines
BibRef
Wang, C.[Chen],
Deng, C.Y.[Cheng-Yuan],
Wang, S.Z.[Su-Zhen],
Imbalance-XGBoost: leveraging weighted and focal losses for binary
label-imbalanced classification with XGBoost,
PRL(136), 2020, pp. 190-197.
Elsevier DOI
2008
Imbalanced classification, XGBoost, Python package
BibRef
Sadhukhan, P.[Payel],
Palit, S.[Sarbani],
Adaptive learning of minority class prior to minority oversampling,
PRL(136), 2020, pp. 16-24.
Elsevier DOI
2008
Class imbalance, Relative neighborhood graph,
Minority set estimation, Oversampling
BibRef
Santos, M.S.[Miriam Seoane],
Abreu, P.H.[Pedro Henriques],
Wilk, S.[Szymon],
Santos, J.[João],
How distance metrics influence missing data imputation with k-nearest
neighbours,
PRL(136), 2020, pp. 111-119.
Elsevier DOI
2008
Missing Data, Data Imputation, k-nearest neighbours,
Distance Functions, Heterogeneous Data, Imbalanced Data
BibRef
Zhu, Q.[Qiuming],
On the performance of Matthews correlation coefficient (MCC) for
imbalanced dataset,
PRL(136), 2020, pp. 71-80.
Elsevier DOI
2008
Matthews correlation coefficient,
Classification accuracy measurement, Performance evaluation, Imbalanced dataset
BibRef
Gao, Y.L.[Yun-Long],
Yang, C.Y.[Cheng-Yu],
Lin, K.Y.[Kuo-Yi],
Pan, J.Y.[Jin-Yan],
Li, L.[Li],
Conditional semi-fuzzy c-means clustering for imbalanced dataset,
IET-IPR(14), No. 11, September 2020, pp. 2343-2355.
DOI Link
2009
BibRef
Naboureh, A.[Amin],
Li, A.[Ainong],
Bian, J.H.[Jin-Hu],
Lei, G.[Guangbin],
Amani, M.[Meisam],
A Hybrid Data Balancing Method for Classification of Imbalanced
Training Data within Google Earth Engine: Case Studies from
Mountainous Regions,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link
2010
BibRef
Naboureh, A.[Amin],
Ebrahimy, H.[Hamid],
Azadbakht, M.[Mohsen],
Bian, J.H.[Jin-Hu],
Amani, M.[Meisam],
RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in
Land Cover Mapping Using Google Earth Engine,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link
2011
BibRef
Jing, X.Y.[Xiao-Yuan],
Zhang, X.Y.[Xin-Yu],
Zhu, X.K.[Xiao-Ke],
Wu, F.[Fei],
You, X.G.[Xin-Ge],
Gao, Y.[Yang],
Shan, S.G.[Shi-Guang],
Yang, J.Y.[Jing-Yu],
Multiset Feature Learning for Highly Imbalanced Data Classification,
PAMI(43), No. 1, January 2021, pp. 139-156.
IEEE DOI
2012
Learning systems, Measurement, Task analysis, Correlation, Training,
Usability, Generative adversarial networks,
weighted uncorrelated constraint
BibRef
Quan, Y.H.[Ying-Hui],
Zhong, X.[Xian],
Feng, W.[Wei],
Chan, J.C.W.[Jonathan Cheung-Wai],
Li, Q.[Qiang],
Xing, M.D.[Meng-Dao],
SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced
Hyperspectral Data Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link
2102
BibRef
Yuan, M.[Min],
Xu, Y.T.[Yi-Tian],
Bound estimation-based safe acceleration for maximum margin of twin
spheres machine with pinball loss,
PR(114), 2021, pp. 107860.
Elsevier DOI
2103
Maximum margin, Pinball loss, Imbalanced data, Bound estimation,
Upper and lower bounds
BibRef
Zhu, Y.[Ye],
Ting, K.M.[Kai Ming],
Carman, M.J.[Mark J.],
Angelova, M.[Maia],
CDF Transform-and-Shift: An effective way to deal with datasets of
inhomogeneous cluster densities,
PR(117), 2021, pp. 107977.
Elsevier DOI
2106
Density-ratio, Density-based clustering, NN Anomaly detection,
Inhomogeneous cluster densities, Scaling, Shift
BibRef
Bejaoui, A.[Amine],
Elkhalil, K.[Khalil],
Kammoun, A.[Abla],
Alouini, M.S.[Mohamed-Slim],
Al-Naffouri, T.[Tareq],
Cost-sensitive design of quadratic discriminant analysis for
imbalanced data,
PRL(149), 2021, pp. 24-29.
Elsevier DOI
2108
Quadratic discriminant analysis, Random matrix theory,
Classification, Imbalanced learning
BibRef
Kim, K.[Kyoungok],
Normalized class coherence change-based kNN for classification of
imbalanced data,
PR(120), 2021, pp. 108126.
Elsevier DOI
2109
NN, Nearest neighbor classification, Imbalanced data, Class coherence
BibRef
Koziarski, M.[Michal],
Potential Anchoring for imbalanced data classification,
PR(120), 2021, pp. 108114.
Elsevier DOI
2109
Machine learning, Classification, Imbalanced data, Oversampling,
Undersampling, Radial basis functions
BibRef
Jing, T.T.[Tao-Tao],
Xu, B.R.[Bing-Rong],
Ding, Z.M.[Zheng-Ming],
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation,
IP(30), 2021, pp. 8200-8211.
IEEE DOI
2110
Training, Task analysis, Adaptation models, Knowledge transfer,
Data models, Training data, Optimization, Transfer learning,
unsupervised domain adaptation
BibRef
Kim, Y.C.[Ye-Chan],
Lee, Y.[Younkwan],
Jeon, M.[Moongu],
Imbalanced image classification with complement cross entropy,
PRL(151), 2021, pp. 33-40.
Elsevier DOI
2110
Loss function, Deep learning, Class imbalance,
Image classification, Complement cross entropy
BibRef
Liu, B.[Bin],
Blekas, K.[Konstantinos],
Tsoumakas, G.[Grigorios],
Multi-label sampling based on local label imbalance,
PR(122), 2022, pp. 108294.
Elsevier DOI
2112
Multi-label learning, Class imbalance,
Oversampling and undersampling, Local label imbalance, Ensemble methods
BibRef
Suh, S.[Sungho],
Lukowicz, P.[Paul],
Lee, Y.O.[Yong Oh],
Discriminative feature generation for classification of imbalanced
data,
PR(122), 2022, pp. 108302.
Elsevier DOI
2112
Imbalanced classification, Generative adversarial networks,
Discriminative feature generation, Transfer learning,
Feature map regularization
BibRef
Maldonado, S.[Sebastián],
Vairetti, C.[Carla],
Fernandez, A.[Alberto],
Herrera, F.[Francisco],
FW-SMOTE: A feature-weighted oversampling approach for imbalanced
classification,
PR(124), 2022, pp. 108511.
Elsevier DOI
2203
Data resampling, SMOTE, OWA Operators, Feature selection,
Imbalanced data classification
BibRef
Hao, J.Y.[Jing-Yu],
Wang, C.J.[Cheng-Jia],
Yang, G.[Guang],
Gao, Z.[Zhifan],
Zhang, J.L.[Jing-Lin],
Zhang, H.[Heye],
Annealing Genetic GAN for Imbalanced Web Data Learning,
MultMed(24), 2022, pp. 1164-1174.
IEEE DOI
2203
Training, Generators, Genetic algorithms, Annealing,
Simulated annealing, Generative adversarial networks,
data augmentation
BibRef
Wang, Z.[Zhe],
Dong, Q.[Qida],
Guo, W.[Wei],
Li, D.D.[Dong-Dong],
Zhang, J.[Jing],
Du, W.L.[Wen-Li],
Geometric imbalanced deep learning with feature scaling and boundary
sample mining,
PR(126), 2022, pp. 108564.
Elsevier DOI
2204
Imbalance problem, Image classification, Geometric information,
Boundary samples mining, Feature scaling
BibRef
Gilet, C.[Cyprien],
Barbosa, S.[Susana],
Fillatre, L.[Lionel],
Discrete Box-Constrained Minimax Classifier for Uncertain and
Imbalanced Class Proportions,
PAMI(44), No. 6, June 2022, pp. 2923-2937.
IEEE DOI
2205
Training, Task analysis, Bayes methods, Robustness, Equalizers,
Medical diagnostic imaging, Support vector machines,
Bayesian robustness
BibRef
Sridhar, S.,
Kalaivani, A.,
Performance Analysis of Two-Stage Iterative Ensemble Method over Random
Oversampling Methods on Multiclass Imbalanced Datasets,
IJIG(22), No. 2, April 2022, pp. 2250025.
DOI Link
2205
BibRef
Wang, G.J.[Guan-Jin],
Zhou, T.[Ta],
Choi, K.S.[Kup-Sze],
Lu, J.[Jie],
A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy
Classifier for Imbalanced Data,
Cyber(52), No. 5, May 2022, pp. 3805-3818.
IEEE DOI
2206
Training, Cancer, Task analysis, Machine learning, Cybernetics,
Prediction algorithms, Information technology,
imbalance learning
BibRef
Chen, J.[Joya],
Liu, D.[Dong],
Luo, B.[Bin],
Peng, X.Z.[Xue-Zheng],
Xu, T.[Tong],
Chen, E.[Enhong],
Residual objectness for imbalance reduction,
PR(130), 2022, pp. 108781.
Elsevier DOI
2206
Object detection, Class imbalance, Residual objectness
BibRef
Quan, D.Y.[Da-Ying],
Feng, W.[Wei],
Dauphin, G.[Gabriel],
Wang, X.F.[Xiao-Feng],
Huang, W.J.[Wen-Jiang],
Xing, M.D.[Meng-Dao],
A Novel Double Ensemble Algorithm for the Classification of
Multi-Class Imbalanced Hyperspectral Data,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Sinha, S.[Saptarshi],
Ohashi, H.[Hiroki],
Nakamura, K.[Katsuyuki],
Class-Difficulty Based Methods for Long-Tailed Visual Recognition,
IJCV(130), No. 10, October 2022, pp. 2517-2531.
Springer DOI
2209
BibRef
Earlier:
Class-wise Difficulty-balanced Loss for Solving Class-imbalance,
ACCV20(VI:549-565).
Springer DOI
2103
BibRef
Sinha, S.[Saptarshi],
Ohashi, H.[Hiroki],
Difficulty-Net:
Learning to Predict Difficulty for Long-Tailed Recognition,
WACV23(6433-6442)
IEEE DOI
2302
Training, Codes, Tail, Predictive models,
Algorithms: Machine learning architectures, formulations,
ethical computer vision
BibRef
Chen, L.[Ling],
Wang, Y.H.[Yu-Hong],
Li, H.[Hao],
Enhancement of DNN-based multilabel classification by grouping labels
based on data imbalance and label correlation,
PR(132), 2022, pp. 108964.
Elsevier DOI
2209
Multilabel classification, data imbalance, label correlation, neural network
BibRef
Zhao, L.C.[Lin-Chang],
Shang, Z.W.[Zhao-Wei],
Tan, J.[Jin],
Zhou, M.L.[Ming-Liang],
Zhang, M.[Mu],
Gu, D.D.[David Dagang],
Zhang, T.P.[Tai-Ping],
Tang, Y.Y.[Yuan Yan],
Siamese networks with an online reweighted example for imbalanced
data learning,
PR(132), 2022, pp. 108947.
Elsevier DOI
2209
Few-shot learning, Reweighted example learning, Data mining, Imbalanced learning
BibRef
Wang, W.Q.[Wei-Qiu],
Zhao, Z.C.[Zhi-Cheng],
Wang, P.[Pingyu],
Su, F.[Fei],
Meng, H.Y.[Hong-Ying],
Attentive Feature Augmentation for Long-Tailed Visual Recognition,
CirSysVideo(32), No. 9, September 2022, pp. 5803-5816.
IEEE DOI
2209
Visualization, Head, Image recognition, Task analysis,
Feature extraction, Data models, Training, Image classification,
data synthesizing
BibRef
Zhang, M.L.[Ming-Liang],
Zhang, X.Y.[Xu-Yao],
Wang, C.[Chuang],
Liu, C.L.[Cheng-Lin],
Towards prior gap and representation gap for long-tailed recognition,
PR(133), 2023, pp. 109012.
Elsevier DOI
2210
Long-tailed learning, Prior gap, Representation gap, Image recognition
BibRef
Liu, Y.X.[Yong-Xu],
Liu, Y.[Yan],
Yu, B.X.B.[Bruce X.B.],
Zhong, S.H.[Sheng-Hua],
Hu, Z.J.[Zhe-Jing],
Noise-robust oversampling for imbalanced data classification,
PR(133), 2023, pp. 109008.
Elsevier DOI
2210
Imbalanced learning, Classification, Clustering
BibRef
Ren, J.J.[Jin-Jun],
Wang, Y.P.[Yu-Ping],
Cheung, Y.M.[Yiu-Ming],
Gao, X.Z.[Xiao-Zhi],
Guo, X.F.[Xiao-Fang],
Grouping-based Oversampling in Kernel Space for Imbalanced Data
Classification,
PR(133), 2023, pp. 108992.
Elsevier DOI
2210
Imbalanced data classification, Kernel method,
Support vector machine, Oversampling
BibRef
Liu, C.L.[Chien-Liang],
Chang, Y.H.[Yu-Hua],
Learning From Imbalanced Data With Deep Density Hybrid Sampling,
SMCS(52), No. 11, November 2022, pp. 7065-7077.
IEEE DOI
2210
Boosting, Training, Euclidean distance, Sampling methods, Costs,
Hybrid power systems, Estimation, Class imbalance, synthetic data
BibRef
Datta, D.[Debaleena],
Mallick, P.K.[Pradeep Kumar],
Reddy, A.V.N.[Annapareddy V. N.],
Mohammed, M.A.[Mazin Abed],
Jaber, M.M.[Mustafa Musa],
Alghawli, A.S.[Abed Saif],
Al-Qaness, M.A.A.[Mohammed A. A.],
A Hybrid Classification of Imbalanced Hyperspectral Images Using
ADASYN and Enhanced Deep Subsampled Multi-Grained Cascaded Forest,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link
2210
BibRef
Zhu, Q.Q.[Qi-Qi],
Deng, W.H.[Wei-Huan],
Zheng, Z.[Zhuo],
Zhong, Y.F.[Yan-Fei],
Guan, Q.F.[Qing-Feng],
Lin, W.H.[Wei-Hua],
Zhang, L.P.[Liang-Pei],
Li, D.R.[De-Ren],
A Spectral-Spatial-Dependent Global Learning Framework for
Insufficient and Imbalanced Hyperspectral Image Classification,
Cyber(52), No. 11, November 2022, pp. 11709-11723.
IEEE DOI
2211
Feature extraction, Training, Field programmable gate arrays,
Data mining, Hyperspectral imaging, Deep learning, Convolution,
patchwise
BibRef
Rodríguez-Alvarez, Y.[Yanela],
García-Lorenzo, M.M.[María Matilde],
Caballero-Mota, Y.[Yailé],
Filiberto-Cabrera, Y.[Yaima],
García-Hilarión, I.M.[Isabel M.],
Machado-Montes-de Oca, D.[Daniela],
Bello Pérez, R.[Rafael],
Fuzzy prototype selection-based classifiers for imbalanced data. Case
study,
PRL(163), 2022, pp. 183-190.
Elsevier DOI
2212
Fuzzy learning, Prototype classifiers, Imbalanced Data
BibRef
Gutiérrez-López, A.[Aitor],
González-Serrano, F.J.[Francisco-Javier],
Figueiras-Vidal, A.R.[Aníbal R.],
Optimum Bayesian thresholds for rebalanced classification problems
using class-switching ensembles,
PR(135), 2023, pp. 109158.
Elsevier DOI
2212
Bayesian framework, Ensembles, Rebalancing techniques,
Imbalanced classification, Label switching
BibRef
Naji, H.A.H.[Hasan A. H.],
Li, T.F.[Tian-Feng],
Xue, Q.J.[Qing-Ji],
Duan, X.D.[Xin-Dong],
A Hypered Deep-Learning-Based Model of Hyperspectral Images
Generation and Classification for Imbalanced Data,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
Shaik, A.L.H.P.[Abdul Lateef Haroon Phulara],
Manoharan, M.K.[Monica Komala],
Pani, A.K.[Alok Kumar],
Avala, R.R.[Raji Reddy],
Chen, C.M.[Chien-Ming],
Gaussian Mutation-Spider Monkey Optimization (GM-SMO) Model for
Remote Sensing Scene Classification,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link
2212
BibRef
van Duynhoven, A.[Alysha],
Dragicevic, S.[Suzana],
Mitigating Imbalance of Land Cover Change Data for Deep Learning
Models with Temporal and Spatiotemporal Sample Weighting Schemes,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link
2301
BibRef
Cao, C.Z.[Chun-Zheng],
Liu, X.[Xin],
Cao, S.[Shuren],
Shi, J.Q.[Jian Qing],
Joint classification and prediction of random curves using
heavy-tailed process functional regression,
PR(136), 2023, pp. 109213.
Elsevier DOI
2301
Functional data analysis, Outliers, Heavy-tailed process,
Bayesian estimation, MCMC
BibRef
Liu, J.H.[Jia-Hang],
Feng, R.[Ruilei],
Chen, P.[Peng],
Wang, X.Z.[Xiao-Zhen],
Ni, Y.[Yue],
Dynamic Loss Reweighting Method Based on Cumulative Classification
Scores for Long-Tailed Remote Sensing Image Classification,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link
2301
BibRef
Wang, X.Y.[Xin-Yue],
Jing, L.P.[Li-Ping],
Lyu, Y.L.[Yi-Lin],
Guo, M.Z.[Ming-Zhe],
Wang, J.Q.[Jia-Qi],
Liu, H.F.[Hua-Feng],
Yu, J.[Jian],
Zeng, T.Y.[Tie-Yong],
Deep Generative Mixture Model for Robust Imbalance Classification,
PAMI(45), No. 3, March 2023, pp. 2897-2912.
IEEE DOI
2302
BibRef
Earlier: A1, A3, A2, Only:
Deep Generative Model for Robust Imbalance Classification,
CVPR20(14112-14121)
IEEE DOI
2008
Perturbation methods, Data models, Uncertainty, Codes, Training,
Predictive models, Training data, Deep generative mixture model,
model perturbation.
Data models.
BibRef
Cui, J.Q.[Jie-Quan],
Liu, S.[Shu],
Tian, Z.T.[Zhuo-Tao],
Zhong, Z.S.[Zhi-Sheng],
Jia, J.Y.[Jia-Ya],
ResLT: Residual Learning for Long-Tailed Recognition,
PAMI(45), No. 3, March 2023, pp. 3695-3706.
IEEE DOI
2302
Tail, Head, Training, Magnetic heads, Image recognition, Transfer learning,
Representation learning, Residual learning, long-tailed recognition
BibRef
Lázaro, M.[Marcelino],
Figueiras-Vidal, A.R.[Aníbal R.],
Neural network for ordinal classification of imbalanced data by
minimizing a Bayesian cost,
PR(137), 2023, pp. 109303.
Elsevier DOI
2302
Bayes cost, Parzen windows, Ordinal classification, Imbalanced
BibRef
Wang, X.N.[Xin-Ning],
Zhao, Y.[Yuben],
Li, C.[Chong],
Ren, P.[Peng],
ProbSAP: A comprehensive and high-performance system for student
academic performance prediction,
PR(137), 2023, pp. 109309.
Elsevier DOI
2302
Student academic performance, SAP prediction,
Educational data mining (EDM), Imbalanced data management,
XGBoost-Enhanced method
BibRef
Liu, Y.C.[Yan-Chen],
Lai, K.W.C.[King Wai Chiu],
The Performance Index of Convolutional Neural Network-Based
Classifiers in Class Imbalance Problem,
PR(137), 2023, pp. 109284.
Elsevier DOI
2302
Deep Learning, Convolutional Neural Network, Class Imbalance,
Class Balance Index, Model Performance Index
BibRef
Zhao, X.Q.[Xin-Qiao],
Xiao, J.[Jimin],
Yu, S.Y.[Si-Yue],
Li, H.[Hui],
Zhang, B.F.[Bing-Feng],
Weight-guided class complementing for long-tailed image recognition,
PR(138), 2023, pp. 109374.
Elsevier DOI
2303
Image recognition, Long-tailed distribution, Gradient shift,
Weight-guided method
BibRef
Li, M.K.[Meng-Ke],
Cheung, Y.M.[Yiu-Ming],
Hu, Z.K.[Zhi-Kai],
Key Point Sensitive Loss for Long-Tailed Visual Recognition,
PAMI(45), No. 4, April 2023, pp. 4812-4825.
IEEE DOI
2303
Tail, Training, Head, Optimization, Visualization, Magnetic heads,
Training data, Long-tailed classification,
imbalance learning
BibRef
Xi, B.[Bobo],
Li, J.J.[Jiao-Jiao],
Diao, Y.[Yan],
Li, Y.S.[Yun-Song],
Li, Z.[Zan],
Huang, Y.[Yan],
Chanussot, J.[Jocelyn],
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced
Hyperspectral Imagery,
CirSysVideo(33), No. 4, April 2023, pp. 1535-1548.
IEEE DOI
2304
Training, Hyperspectral imaging,
Feature extraction, Data models, Decoding, Benchmark testing,
hyperspectral image classification
BibRef
Tiong, A.M.H.[Anthony Meng Huat],
Li, J.[Junnan],
Lin, G.S.[Guo-Sheng],
Li, B.Y.[Bo-Yang],
Xiong, C.M.[Cai-Ming],
Hoi, S.C.H.[Steven C.H.],
Improving Tail-Class Representation with Centroid Contrastive
Learning,
PRL(168), 2023, pp. 123-130.
Elsevier DOI
2304
Long-tailed classification, Imbalanced learning,
Contrastive learning, Deep learning
BibRef
Chen, H.[Huanfa],
Cheng, Y.[Yan],
Travel Mode Choice Prediction Using Imbalanced Machine Learning,
ITS(24), No. 4, April 2023, pp. 3795-3808.
IEEE DOI
2304
Predictive models, Machine learning, Measurement, Support vector machines,
Testing, Neural networks, Data models, travel mode choice
BibRef
Xiang, L.[Liuyu],
Han, J.G.[Jun-Gong],
Ding, G.[Guiguang],
Margin-aware rectified augmentation for long-tailed recognition,
PR(141), 2023, pp. 109608.
Elsevier DOI
2306
Long-tailed recognition, Data augmentation, Mixup
BibRef
Wang, S.[Shuang],
Chen, H.[Hui],
Ding, L.[Lei],
Sui, H.[He],
Ding, J.L.[Jian-Li],
GAN-SR Anomaly Detection Model Based on Imbalanced Data,
IEICE(E106-D), No. 7, July 2023, pp. 1209-1218.
WWW Link.
2307
BibRef
Rosales-Pérez, A.[Alejandro],
García, S.[Salvador],
Herrera, F.[Francisco],
Handling Imbalanced Classification Problems With Support Vector
Machines via Evolutionary Bilevel Optimization,
Cyber(53), No. 8, August 2023, pp. 4735-4747.
IEEE DOI
2307
Optimization, Support vector machines, Costs, Kernel, Training,
Search problems, Inference algorithms,
support vector machines (SVMs)
BibRef
Islam, M.T.[Md Touhid],
Islam, M.R.[Md Rashedul],
Uddin, M.P.[Md Palash],
Ulhaq, A.[Anwaar],
A Deep Learning-Based Hyperspectral Object Classification Approach
via Imbalanced Training Samples Handling,
RS(15), No. 14, 2023, pp. 3532.
DOI Link
2307
BibRef
Gong, H.Y.[Hui-Yun],
Li, Y.G.[Ye-Guang],
Dong, J.[Jian],
A dual-balanced network for long-tail distribution object detection,
IET-CV(17), No. 5, 2023, pp. 565-575.
DOI Link
2309
learning (artificial intelligence), object detection
BibRef
Guan, Q.J.[Qing-Ji],
Li, Z.Z.[Zhuang-Zhuang],
Zhang, J.[Jiayu],
Huang, Y.P.[Ya-Ping],
Zhao, Y.[Yao],
Joint representation and classifier learning for long-tailed image
classification,
IVC(137), 2023, pp. 104759.
Elsevier DOI
2309
Long-tailed image classification, Representation learning,
Classifier learning, Supervised contrastive learning
BibRef
Kim, D.J.[Dong-Jin],
Ke, T.W.[Tsung-Wei],
Yu, S.X.[Stella X.],
Local pseudo-attributes for long-tailed recognition,
PRL(172), 2023, pp. 51-57.
Elsevier DOI
2309
Long-tailed recognition, Pseudo-attributes, Self-supervised learning
BibRef
Zhang, Y.F.[Yi-Fan],
Kang, B.Y.[Bing-Yi],
Hooi, B.[Bryan],
Yan, S.C.[Shui-Cheng],
Feng, J.S.[Jia-Shi],
Deep Long-Tailed Learning: A Survey,
PAMI(45), No. 9, September 2023, pp. 10795-10816.
IEEE DOI
2309
Survey, Long-Tailed.
BibRef
Tan, J.[Jingru],
Li, B.[Bo],
Lu, X.[Xin],
Yao, Y.Q.[Yong-Qiang],
Yu, F.W.[Feng-Wei],
He, T.[Tong],
Ouyang, W.L.[Wan-Li],
The Equalization Losses: Gradient-Driven Training for Long-tailed
Object Recognition,
PAMI(45), No. 11, November 2023, pp. 13876-13892.
IEEE DOI
2310
BibRef
Yang, J.X.[Jia-Xin],
Yu, M.M.[Miao-Miao],
Li, S.[Shuohao],
Zhang, J.[Jun],
Hu, S.Z.[Sheng-Ze],
Long-Tailed Object Detection for Multimodal Remote Sensing Images,
RS(15), No. 18, 2023, pp. 4539.
DOI Link
2310
BibRef
Soltanzadeh, P.[Paria],
Feizi-Derakhshi, M.R.[M. Reza],
Hashemzadeh, M.[Mahdi],
Addressing the class-imbalance and class-overlap problems by a
metaheuristic-based under-sampling approach,
PR(143), 2023, pp. 109721.
Elsevier DOI
2310
Imbalanced classification, Imbalanced datasets, Class overlap,
Class imbalance, Metaheuristic algorithms, Under-sampling
BibRef
Kong, X.Y.[Xiang-Yuan],
Wei, X.[Xiang],
Liu, X.Y.[Xiao-Yu],
Wang, J.J.[Jing-Jie],
Xing, W.W.[Wei-Wei],
Lu, W.[Wei],
FGBC: Flexible graph-based balanced classifier for class-imbalanced
semi-supervised learning,
PR(143), 2023, pp. 109793.
Elsevier DOI
2310
Semi-supervised learning, Class-imbalanced learning,
Graph network, Label propagation, MixUp
BibRef
Sun, J.[Junyao],
Zhou, J.K.[Jing-Kai],
Liu, Q.[Qiong],
PoiseNet: Dealing With Data Imbalance in DensePose,
CirSysVideo(33), No. 10, October 2023, pp. 5664-5678.
IEEE DOI
2310
BibRef
Zhou, X.S.[Xue-Song],
Zhai, J.H.[Jun-Hai],
Cao, Y.[Yang],
Feature fusion network for long-tailed visual recognition,
PR(144), 2023, pp. 109827.
Elsevier DOI
2310
Long-tailed learning, Head and tail classes,
Feature representations, Feature fusion network
BibRef
Liu, W.[Weide],
Wu, Z.H.[Zhong-Hua],
Wang, Y.M.[Yi-Ming],
Ding, H.H.[Heng-Hui],
Liu, F.[Fayao],
Lin, J.[Jie],
Lin, G.S.[Guo-Sheng],
LCReg: Long-tailed image classification with Latent Categories based
Recognition,
PR(145), 2024, pp. 109971.
Elsevier DOI
2311
Long-tailed, Image classification, Latent Categories
BibRef
Zhao, W.[Wei],
Zhao, H.[Hong],
Hierarchical long-tailed classification based on multi-granularity
knowledge transfer driven by multi-scale feature fusion,
PR(145), 2024, pp. 109842.
Elsevier DOI
2311
Long-tailed learning, Hierarchical classification,
Multi-granularity, Multi-scale feature fusion, Knowledge transfer
BibRef
Alexandridis, K.P.[Konstantinos Panagiotis],
Luo, S.[Shan],
Nguyen, A.[Anh],
Deng, J.K.[Jian-Kang],
Zafeiriou, S.[Stefanos],
Inverse Image Frequency for Long-Tailed Image Recognition,
IP(32), 2023, pp. 5721-5736.
IEEE DOI Code:
WWW Link.
2311
BibRef
Tan, Z.C.[Zi-Chang],
Li, J.[Jun],
Du, J.[Jinhao],
Wan, J.[Jun],
Lei, Z.[Zhen],
Guo, G.D.[Guo-Dong],
NCL++: Nested Collaborative Learning for long-tailed visual
recognition,
PR(147), 2024, pp. 110064.
Elsevier DOI
2312
BibRef
Earlier: A2, A1, A4, A5, A6, Only:
Nested Collaborative Learning for Long-Tailed Visual Recognition,
CVPR22(6939-6948)
IEEE DOI
2210
Long-tailed visual recognition, Collaborative learning,
Online distillation, Deep learning.
Training, Representation learning, Visualization, Uncertainty, Codes,
Supervised learning, Transfer/low-shot/long-tail learning, retrieval
BibRef
Schultz, K.[Kristian],
Bej, S.[Saptarshi],
Hahn, W.[Waldemar],
Wolfien, M.[Markus],
Srivastava, P.[Prashant],
Wolkenhauer, O.[Olaf],
ConvGeN: A convex space learning approach for deep-generative
oversampling and imbalanced classification of small tabular datasets,
PR(147), 2024, pp. 110138.
Elsevier DOI
2312
Imbalanced data, Convex space learning, LoRAS, GAN, Tabular data
BibRef
Baik, J.S.[Jae Soon],
Yoon, I.Y.[In Young],
Choi, J.W.[Jun Won],
DBN-Mix: Training dual branch network using bilateral mixup
augmentation for long-tailed visual recognition,
PR(147), 2024, pp. 110107.
Elsevier DOI
2312
Long-tailed visual recognition, Class imbalance,
Image classification, Mixup augmentation, Temperature scaling
BibRef
Jabbari, H.[Hamed],
Bigdeli, N.[Nooshin],
A new hierarchical algorithm based on CapsGAN for imbalanced image
classification,
IET-IPR(18), No. 1, 2024, pp. 194-210.
DOI Link
2401
capsule network, data augmentation, deep Learning,
generative adversarial networks, imbalanced image classification
BibRef
Du, Y.J.[Ying-Jun],
Sun, H.L.[Hao-Liang],
Zhen, X.T.[Xian-Tong],
Xu, J.[Jun],
Yin, Y.L.[Yi-Long],
Shao, L.[Ling],
Snoek, C.G.M.[Cees G. M.],
MetaKernel: Learning Variational Random Features With Limited Labels,
PAMI(46), No. 3, March 2024, pp. 1464-1478.
IEEE DOI
2402
Task analysis, Kernel, Adaptation models, Prototypes, Optimization,
Neural networks, Memory modules, Meta learning, few-shot learning,
random features
BibRef
Du, Y.J.[Ying-Jun],
Shen, J.Y.[Jia-Yi],
Zhen, X.T.[Xian-Tong],
Snoek, C.G.M.[Cees G. M.],
SuperDisco: Super-Class Discovery Improves Visual Recognition for the
Long-Tail,
CVPR23(19944-19954)
IEEE DOI
2309
BibRef
Xu, Z.Z.[Zheng-Zhuo],
Chai, Z.H.[Zeng-Hao],
Xu, C.Y.[Cheng-Yin],
Yuan, C.[Chun],
Yang, H.Q.[Hai-Qin],
Towards Effective Collaborative Learning in Long-Tailed Recognition,
MultMed(26), 2024, pp. 3754-3764.
IEEE DOI
2402
Tail, Federated learning, Task analysis, Uncertainty, Training, Head,
Feature extraction, Image classification, long tail recognition,
knowledge distillation
BibRef
Farhadpour, S.[Sarah],
Warner, T.A.[Timothy A.],
Maxwell, A.E.[Aaron E.],
Selecting and Interpreting Multiclass Loss and Accuracy Assessment
Metrics for Classifications with Class Imbalance: Guidance and Best
Practices,
RS(16), No. 3, 2024, pp. 533.
DOI Link
2402
BibRef
Ma, Y.[Yanbiao],
Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
Yang, S.Y.[Shu-Yuan],
Liu, X.[Xu],
Chen, P.[Puhua],
Feature Distribution Representation Learning Based on Knowledge
Transfer for Long-Tailed Classification,
MultMed(26), 2024, pp. 2772-2784.
IEEE DOI
2402
Tail, Training, Head, Feature extraction, Knowledge transfer,
Representation learning, Noise measurement,
knowledge transfer
BibRef
Elbatel, M.[Marawan],
Martí, R.[Robert],
Li, X.M.[Xiao-Meng],
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for
Long-Tailed Medical Image Recognition,
MedImg(43), No. 3, March 2024, pp. 954-965.
IEEE DOI Code:
WWW Link.
2403
Biomedical imaging, Adaptation models, Task analysis, Tuning,
Data models, Transformers, Image classification, Visual prompting,
long tailed learning
BibRef
Wang, W.Q.[Wei-Qiu],
Chen, Z.[Zining],
Su, F.[Fei],
Zhao, Z.C.[Zhi-Cheng],
Text-guided Fourier Augmentation for long-tailed recognition,
PRL(179), 2024, pp. 38-44.
Elsevier DOI
2403
Long-tailed visual recognition, Language models,
Fourier transform, Imbalanced data
BibRef
Chen, J.H.[Jia-Hao],
Su, B.[Bing],
Instance-Specific Semantic Augmentation for Long-Tailed Image
Classification,
IP(33), 2024, pp. 2544-2557.
IEEE DOI
2404
Tail, Semantics, Head, Programmable logic arrays, Training,
Image classification, Reliability, Long-tailed distribution,
imbalanced data
BibRef
Zhang, S.Y.[Shao-Yu],
Chen, C.[Chen],
Xie, Q.[Qiong],
Sun, H.G.[Hai-Gang],
Dong, F.[Fei],
Peng, S.[Silong],
Distribution Unified and Probability Space Aligned Teacher-Student
Learning for Imbalanced Visual Recognition,
CirSysVideo(34), No. 4, April 2024, pp. 2414-2425.
IEEE DOI
2404
Training, Predictive models, Smoothing methods, Data models,
Visualization, Training data, Sun, Class-imbalanced learning,
teacher-student learning
BibRef
Liu, H.F.[Hua-Feng],
Sheng, M.M.[Meng-Meng],
Sun, Z.[Zeren],
Yao, Y.Z.[Ya-Zhou],
Hua, X.S.[Xian-Sheng],
Shen, H.T.[Heng-Tao],
Learning With Imbalanced Noisy Data by Preventing Bias in Sample
Selection,
MultMed(26), 2024, pp. 7426-7437.
IEEE DOI
2405
Noise measurement, Training, Tail, Predictive models, Data models, Sun,
Self-supervised learning, Imbalanced label noise,
average confidence margin
BibRef
Guo, X.Y.[Xiao-Yu],
Wei, X.[Xiang],
Zhang, S.L.[Shun-Li],
Lu, W.[Wei],
Xing, W.W.[Wei-Wei],
DCRP: Class-Aware Feature Diffusion Constraint and Reliable
Pseudo-Labeling for Imbalanced Semi-Supervised Learning,
MultMed(26), 2024, pp. 7146-7159.
IEEE DOI
2405
Training, Feature extraction, Semisupervised learning, Reliability,
Data models, Data augmentation, Tail, Class-imbalanced learning,
semi-supervised learning
BibRef
Zhao, Q.H.[Qi-Hao],
Zhang, F.[Fan],
Hu, W.[Wei],
Feng, S.[Songhe],
Liu, J.[Jun],
OHD: An Online Category-Aware Framework for Learning With Noisy
Labels Under Long-Tailed Distribution,
CirSysVideo(34), No. 5, May 2024, pp. 3806-3818.
IEEE DOI
2405
Noise measurement, Training, Tail, Frequency estimation,
Uncertainty, Robustness, Deep neural networks, image classification
BibRef
Ma, Y.B.[Yan-Biao],
Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
Yang, S.Y.[Shu-Yuan],
Liu, X.[Xu],
Chen, P.H.[Pu-Hua],
Geometric Prior Guided Feature Representation Learning for Long-Tailed
Classification,
IJCV(132), No. 7, July 2024, pp. Pages2493-2510.
Springer DOI
2406
BibRef
Ma, Y.B.[Yan-Biao],
Jiao, L.C.[Li-Cheng],
Liu, F.[Fang],
Yang, S.Y.[Shu-Yuan],
Liu, X.[Xu],
Li, L.L.[Ling-Ling],
Curvature-Balanced Feature Manifold Learning for Long-Tailed
Classification,
CVPR23(15824-15835)
IEEE DOI
2309
BibRef
Li, X.J.[Xiao-Jun],
Su, Y.[Yi],
Yao, J.P.[Jun-Ping],
Guo, Y.[Yi],
Fan, S.[Shuai],
Factor annealing decoupling compositional training method for
imbalanced hyperspectral image classification,
IET-IPR(18), No. 10, 2024, pp. 2553-2567.
DOI Link
2408
image classification, image processing, image representation,
learning (artificial intelligence), pattern classification, remote sensing
BibRef
Pan, H.L.[Hao-Lin],
Guo, Y.[Yong],
Yu, M.[Mianjie],
Chen, J.[Jian],
Enhanced Long-Tailed Recognition With Contrastive CutMix Augmentation,
IP(33), 2024, pp. 4215-4230.
IEEE DOI Code:
WWW Link.
2408
BibRef
Han, M.M.[Ming-Ming],
Guo, H.[Husheng],
Wang, W.J.[Wen-Jian],
A new data complexity measure for multi-class imbalanced
classification tasks,
PR(157), 2025, pp. 110881.
Elsevier DOI
2409
Data characteristic, Skewed distribution, Correlation, Multi-class
BibRef
Pang, Y.[Ying],
Peng, L.Z.[Li-Zhi],
Zhang, H.B.[Hai-Bo],
Chen, Z.X.[Zhen-Xiang],
Yang, B.[Bo],
Imbalanced ensemble learning leveraging a novel data-level diversity
metric,
PR(157), 2025, pp. 110886.
Elsevier DOI
2409
Diversity measurement, Imbalanced learning, Classification
BibRef
Liu, W.H.[Wei-Hua],
Liu, X.B.[Xia-Bi],
Li, H.Y.[Hui-Yu],
Lin, C.C.[Chao-Chao],
Contraction mapping of feature norms for data quality imbalance
learning,
PRL(185), 2024, pp. 232-238.
Elsevier DOI Code:
WWW Link.
2410
Softmax loss, Quality imbalance learning, Image classification
BibRef
Qu, A.[Aixi],
Wu, Q.[Qiang],
Yu, L.[Luyue],
Li, J.[Jing],
Liu, J.[Ju],
Class-Specific Thresholding for Imbalanced Semi-Supervised Learning,
SPLetters(31), 2024, pp. 2375-2379.
IEEE DOI
2410
Training, Predictive models, Data models, Computational modeling,
Thresholding (Imaging), Accuracy, Sensitivity, Deep learning, class imbalance
BibRef
Xie, H.T.[Hong-Tao],
Jiang, Y.[Yan],
Zhang, L.[Lei],
Li, P.D.[Pan-Deng],
Zhang, D.M.[Dong-Ming],
Zhang, Y.D.[Yong-Dong],
Semantic-Enhanced Proxy-Guided Hashing for Long-Tailed Image
Retrieval,
MultMed(26), 2024, pp. 9499-9514.
IEEE DOI
2410
Semantics, Codes, Tail, Measurement, Training, Image retrieval,
Covariance matrices, Deep hashing, long-tailed learning, similarity measuring
BibRef
Zhang, E.[Enhao],
Geng, C.X.[Chuan-Xing],
Li, C.O.[Cha-Ohua],
Chen, S.C.[Song-Can],
Dynamic Learnable Logit Adjustment for Long-Tailed Visual Recognition,
CirSysVideo(34), No. 9, September 2024, pp. 7986-7997.
IEEE DOI
2410
Tail, Training, Prototypes, Magnetic heads, Visualization, Standards,
Vectors, Long-tailed learning, logit adjustment, algebraic rank, neural collapse
BibRef
Pan, C.[Cong],
Peng, J.[Junran],
Bu, X.Y.[Xing-Yuan],
Zhang, Z.X.[Zhao-Xiang],
Large-Scale Object Detection in the Wild With Imbalanced Data
Distribution, and Multi-Labels,
PAMI(46), No. 12, December 2024, pp. 9255-9271.
IEEE DOI
2411
Object detection, Annotations, Training, Detectors, Deep learning,
Toy manufacturing industry, Automobiles, object detection,
multi-labels
BibRef
Peng, J.[Junran],
Bu, X.Y.[Xing-Yuan],
Sun, M.,
Zhang, Z.X.[Zhao-Xiang],
Tan, T.,
Yan, J.,
Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels,
CVPR20(9706-9715)
IEEE DOI
2008
Object detection, Training, Machine learning, Automobiles,
Toy manufacturing industry, Sampling methods, Detectors
BibRef
Xu, N.[Ning],
Qiao, C.Y.[Cong-Yu],
Zhao, Y.C.[Yu-Chen],
Geng, X.[Xin],
Zhang, M.L.[Min-Ling],
Variational Label Enhancement for Instance-Dependent Partial Label
Learning,
PAMI(46), No. 12, December 2024, pp. 11298-11313.
IEEE DOI
2411
Training, Phase locked loops, Predictive models,
Mutual information, Labeling, Benchmark testing, Data augmentation,
partial-label learning
BibRef
Saadatmand, H.,
Akbarzadeh-T, M.R.[Mohammad-R.],
Many-Objective Jaccard-Based Evolutionary Feature Selection for
High-Dimensional Imbalanced Data Classification,
PAMI(46), No. 12, December 2024, pp. 8820-8835.
IEEE DOI
2411
Accuracy, Measurement, Optimization, Feature extraction,
Classification algorithms, Statistics, Sociology,
wrapper method
BibRef
Zhang, H.W.[Hong-Wei],
Du, Q.Y.[Qing-Yun],
Zhang, S.[Shuai],
Yang, R.F.[Ren-Fei],
A Semantically Enhanced Label Prediction Method for Imbalanced POI
Data Category Distribution,
IJGI(13), No. 10, 2024, pp. 364.
DOI Link
2411
BibRef
Yue, X.[Xinli],
Mou, N.P.[Ning-Ping],
Wang, Q.[Qian],
Zhao, L.C.[Ling-Chen],
Revisiting Adversarial Training Under Long-Tailed Distributions,
CVPR24(24492-24501)
IEEE DOI Code:
WWW Link.
2410
Training, Codes, Computational modeling, Training data,
Computer architecture, Data augmentation
BibRef
Khorram, S.[Saeed],
Jiang, M.Q.[Ming-Qi],
Shahbazi, M.[Mohamad],
Danesh, M.H.[Mohamad H.],
Fuxin, L.[Li],
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via
Unconditional Training at Lower Resolutions,
CVPR24(7580-7590)
IEEE DOI Code:
WWW Link.
2410
Training, Measurement, Image resolution, Head, Image synthesis,
Training data, Tail, long-tail, GAN, long-tail generative learning,
class imbalance
BibRef
Cetinkaya, B.[Bedrettin],
Kalkan, S.[Sinan],
Akbas, E.[Emre],
RankED: Addressing Imbalance and Uncertainty in Edge Detection Using
Ranking-based Losses,
CVPR24(3239-3249)
IEEE DOI Code:
WWW Link.
2410
Training, Uncertainty, Codes, Image edge detection, Detectors,
edge detection, uncertainty, imbalance
BibRef
Lemkhenter, A.[Abdelhak],
Wang, M.[Manchen],
Zancato, L.[Luca],
Swaminathan, G.[Gurumurthy],
Favaro, P.[Paolo],
Modolo, D.[Davide],
SemiGPC: Distribution-Aware Label Refinement for Imbalanced
Semi-Supervised Learning Using Gaussian Processes,
L3D-IVU24(2576-2585)
IEEE DOI
2410
Sensitivity, Accuracy, Gaussian processes, Semisupervised learning,
Predictive models, semi-supervised learning, Gaussian processes
BibRef
Wang, X.[Xi],
Yang, X.[Xu],
Yin, J.[Jie],
Wei, K.[Kun],
Deng, C.[Cheng],
Long-Tail Class Incremental Learning via Independent SUb-Prototype
Construction,
CVPR24(28598-28607)
IEEE DOI
2410
Incremental learning, Extraterrestrial phenomena,
Computational modeling, Collaboration, Benchmark testing, Vectors
BibRef
Han, P.X.[Peng-Xiao],
Ye, C.[Changkun],
Zhou, J.[Jieming],
Zhang, J.[Jing],
Hong, J.[Jie],
Li, X.S.[Xue-Song],
Latent-based Diffusion Model for Long-tailed Recognition,
L3D-IVU24(2639-2648)
IEEE DOI
2410
Training, Computational modeling, Transfer learning,
Noise reduction, Tail, diffusion model, long-tailed recognition,
imbalance distribution
BibRef
Lee, H.[Hyuck],
Kim, H.[Heeyoung],
CDMAD: Class-Distribution-Mismatch-Aware Debiasing for
Class-Imbalanced Semi-Supervised Learning,
CVPR24(23891-23900)
IEEE DOI
2410
Training, Color, Semisupervised learning, Benchmark testing,
Prediction algorithms, Solids, Semi-supervised learning,
Semi-supervised long-tailed learning
BibRef
Lavoie, M.A.[Marc-Antoine],
Waslander, S.L.[Steven L.],
Class Instance Balanced Learning for Long-Tailed Classification,
CRV23(121-128)
IEEE DOI
2406
Training, Head, Training data, Tail, Artificial neural networks,
Task analysis, Robots, classification, contrastive learning,
long tailed classification
BibRef
Ye, C.[Changkun],
Tsuchida, R.[Russell],
Petersson, L.[Lars],
Barnes, N.M.[Nick M.],
Label Shift Estimation for Class-Imbalance Problem:
A Bayesian Approach,
WACV24(1062-1071)
IEEE DOI Code:
WWW Link.
2404
Adaptation models, Monte Carlo methods, Codes,
Computational modeling, Estimation, Data models, Algorithms,
Image recognition and understanding
BibRef
Kalla, J.[Jayateja],
Biswas, S.[Soma],
Robust Feature Learning and Global Variance-Driven Classifier
Alignment for Long-Tail Class Incremental Learning,
WACV24(32-41)
IEEE DOI Code:
WWW Link.
2404
Representation learning, Power measurement, Codes, Prototypes, Tail,
Data models, Algorithms, Machine learning architectures,
Image recognition and understanding
BibRef
Zhang, S.[Shan],
Ni, Y.[Yao],
Du, J.[Jinhao],
Liu, Y.X.[Yan-Xia],
Koniusz, P.[Piotr],
Semantic Transfer from Head to Tail: Enlarging Tail Margin for
Long-Tailed Visual Recognition,
WACV24(1339-1349)
IEEE DOI
2404
Training, Visualization, Head, Semantics, Tail, Benchmark testing,
Fasteners, Algorithms, Image recognition and understanding,
Virtual / augmented reality
BibRef
Dixit, A.[Abhishek],
Mani, A.[Ashish],
GeometricSMOTE-Enhanced Deep Gaussian Mixture Models for Imbalanced
Data Classification,
ICCVMI23(1-6)
IEEE DOI
2403
Deep learning, Training, Analytical models, Data analysis, Merging,
Benchmark testing, Probabilistic logic, SMOTE, Class Imbalance,
Imbalance learning
BibRef
Zhao, Y.[Yu],
Wang, N.[Nan],
Parameter selection of Gaussian kernel for cost-sensitive support
vector machines in imbalanced data classification,
CVIDL23(243-249)
IEEE DOI
2403
Support vector machines, Deep learning, Classification algorithms,
Behavioral sciences, Indexes, Kernel, Recall
BibRef
Zhou, Y.X.[Yi-Xuan],
Qu, Y.[Yi],
Xu, X.[Xing],
Shen, H.T.[Heng-Tao],
ImbSAM: A Closer Look at Sharpness-Aware Minimization in
Class-Imbalanced Recognition,
ICCV23(11311-11321)
IEEE DOI Code:
WWW Link.
2401
BibRef
Lu, Y.[Yang],
Zhang, Y.L.[Yi-Liang],
Han, B.[Bo],
Cheung, Y.M.[Yiu-Ming],
Wang, H.Z.[Han-Zi],
Label-Noise Learning with Intrinsically Long-Tailed Data,
ICCV23(1369-1378)
IEEE DOI Code:
WWW Link.
2401
BibRef
Dong, N.[Na],
Zhang, Y.Q.[Yong-Qiang],
Ding, M.L.[Ming-Li],
Lee, G.H.[Gim Hee],
Boosting Long-tailed Object Detection via Step-wise Learning on
Smooth-tail Data,
ICCV23(6917-6926)
IEEE DOI Code:
WWW Link.
2401
BibRef
Tao, Y.[Yingfan],
Sun, J.[Jingna],
Yang, H.[Hao],
Chen, L.[Li],
Wang, X.[Xu],
Yang, W.M.[Wen-Ming],
Du, D.[Daniel],
Zheng, M.[Min],
Local and Global Logit Adjustments for Long-Tailed Learning,
ICCV23(11749-11758)
IEEE DOI
2401
BibRef
Zhang, S.[Shaoyu],
Chen, C.[Chen],
Peng, S.[Silong],
Reconciling Object-Level and Global-Level Objectives for Long-Tail
Detection,
ICCV23(18936-18946)
IEEE DOI Code:
WWW Link.
2401
BibRef
Chen, X.H.[Xiao-Hua],
Zhou, Y.[Yucan],
Wu, D.[Dayan],
Yang, C.[Chule],
Li, B.[Bo],
Hu, Q.H.[Qing-Hua],
Wang, W.P.[Wei-Ping],
AREA: Adaptive Reweighting via Effective Area for Long-Tailed
Classification,
ICCV23(19220-19230)
IEEE DOI Code:
WWW Link.
2401
BibRef
Park, M.H.[Min-Ho],
Kim, H.I.[Hyung-Il],
Song, H.J.[Hwa Jeon],
Kang, D.O.[Dong-Oh],
Augmenting Features via Contrastive Learning-based Generative Model
for Long-Tailed Classification,
LIMIT23(1010-1019)
IEEE DOI
2401
BibRef
Zhao, Q.H.[Qi-Hao],
Jiang, C.[Chen],
Hu, W.[Wei],
Zhang, F.[Fan],
Liu, J.[Jun],
MDCS: More Diverse Experts with Consistency Self-distillation for
Long-tailed Recognition,
ICCV23(11563-11574)
IEEE DOI Code:
WWW Link.
2401
BibRef
Lin, C.S.[Ci-Siang],
Chen, M.H.[Min-Hung],
Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Frequency-Aware Self-Supervised Long-Tailed Learning,
LIMIT23(963-972)
IEEE DOI
2401
BibRef
Park, W.[Wongi],
Park, I.[Inhyuk],
Kim, S.[Sungeun],
Ryu, J.B.[Jong-Bin],
Robust Asymmetric Loss for Multi-Label Long-Tailed Learning,
CVAMD23(2703-2712)
IEEE DOI Code:
WWW Link.
2401
BibRef
Yamagishi, Y.[Yosuke],
Hanaoka, S.[Shohei],
Effect of Stage Training for Long-Tailed Multi-Label Image
Classification,
CVAMD23(2713-2720)
IEEE DOI
2401
BibRef
Zhang, W.Q.[Wen-Qiao],
Liu, C.[Changshuo],
Zeng, L.Z.[Ling-Ze],
Ooi, B.[Bengchin],
Tang, S.L.[Si-Liang],
Zhuang, Y.T.[Yue-Ting],
Learning in Imperfect Environment: Multi-Label Classification with
Long-Tailed Distribution and Partial Labels,
ICCV23(1423-1432)
IEEE DOI Code:
WWW Link.
2401
BibRef
Nápoles, G.[Gonzalo],
Grau, I.[Isel],
Presumably Correct Undersampling,
CIARP23(I:420-433).
Springer DOI
2312
BibRef
Nah, W.J.[Wan Jun],
Ng, C.C.[Chun Chet],
Lin, C.T.[Che-Tsung],
Lee, Y.K.[Yeong Khang],
Kew, J.L.[Jie Long],
Tan, Z.Q.[Zhi Qin],
Chan, C.S.[Chee Seng],
Zach, C.[Christopher],
Lai, S.H.[Shang-Hong],
Rethinking Long-Tailed Visual Recognition with Dynamic Probability
Smoothing and Frequency Weighted Focusing,
ICIP23(435-439)
IEEE DOI Code:
WWW Link.
2312
BibRef
Mei, S.B.[Shi-Bin],
Zhao, C.L.[Cheng-Long],
Yuan, S.C.[Sheng-Chao],
Ni, B.B.[Bing-Bing],
Exploring and Utilizing Pattern Imbalance,
CVPR23(7569-7578)
IEEE DOI
2309
BibRef
Lim, J.[Jongin],
Kim, Y.[Youngdong],
Kim, B.[Byungjai],
Ahn, C.[Chanho],
Shin, J.[Jinwoo],
Yang, E.[Eunho],
Han, S.[Seungju],
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing,
CVPR23(3832-3841)
IEEE DOI
2309
Due to spurious correlations in the training data.
BibRef
Perrett, T.[Toby],
Sinha, S.[Saptarshi],
Burghardt, T.[Tilo],
Mirmehdi, M.[Majid],
Damen, D.[Dima],
Use Your Head: Improving Long-Tail Video Recognition,
CVPR23(2415-2425)
IEEE DOI
2309
BibRef
Wei, T.[Tong],
Gan, K.[Kai],
Towards Realistic Long-Tailed Semi-Supervised Learning:
Consistency is All You Need,
CVPR23(3469-3478)
IEEE DOI
2309
BibRef
Gou, Y.B.[Yuan-Biao],
Hu, P.[Peng],
Lv, J.C.[Jian-Cheng],
Zhu, H.Y.[Hong-Yuan],
Peng, X.[Xi],
Rethinking Image Super Resolution from Long-Tailed Distribution
Learning Perspective,
CVPR23(14327-14336)
IEEE DOI
2309
BibRef
Du, Y.X.[Ying-Xiao],
Wu, J.X.[Jian-Xin],
No One Left Behind: Improving the Worst Categories in Long-Tailed
Learning,
CVPR23(15804-15813)
IEEE DOI
2309
BibRef
Du, F.[Fei],
Yang, P.[Peng],
Jia, Q.[Qi],
Nan, F.T.[Feng-Tao],
Chen, X.T.[Xiao-Ting],
Yang, Y.[Yun],
Global and Local Mixture Consistency Cumulative Learning for
Long-tailed Visual Recognitions,
CVPR23(15814-15823)
IEEE DOI
2309
BibRef
Aimar, E.S.[Emanuel Sanchez],
Jonnarth, A.[Arvi],
Felsberg, M.[Michael],
Kuhlmann, M.[Marco],
Balanced Product of Calibrated Experts for Long-Tailed Recognition,
CVPR23(19967-19977)
IEEE DOI
2309
BibRef
Jin, Y.[Yan],
Li, M.K.[Meng-Ke],
Lu, Y.[Yang],
Cheung, Y.M.[Yiu-Ming],
Wang, H.Z.[Han-Zi],
Long-Tailed Visual Recognition via Self-Heterogeneous Integration
with Knowledge Excavation,
CVPR23(23695-23704)
IEEE DOI
2309
BibRef
Li, J.[Jian],
Meng, Z.[Ziyao],
Shi, D.[Daqian],
Song, R.[Rui],
Diao, X.L.[Xiao-Lei],
Wang, J.W.[Jing-Wen],
Xu, H.[Hao],
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition,
CVPR23(24080-24089)
IEEE DOI
2309
BibRef
Cai, F.[Feng],
Wu, K.Y.[Ke-Yu],
Wang, H.P.[Hai-Peng],
Wang, F.[Feng],
A Three-Stage Framework with Reliable Sample Pool for Long-Tailed
Classification,
PBVS23(479-486)
IEEE DOI
2309
BibRef
Long, H.[Haixu],
Zhang, X.L.[Xiao-Lin],
Liu, Y.[Yanbin],
Luo, Z.[Zongtai],
Liu, J.B.[Jian-Bo],
Mutual Exclusive Modulator for Long-Tailed Recognition,
L3D-IVU23(4891-4900)
IEEE DOI
2309
BibRef
Chen, J.H.[Jia-Hao],
Su, B.[Bing],
Transfer Knowledge from Head to Tail:
Uncertainty Calibration under Long-tailed Distribution,
CVPR23(19978-19987)
IEEE DOI
2309
BibRef
Zhou, Z.P.[Zhi-Peng],
Li, L.Q.[Lan-Qing],
Zhao, P.L.[Pei-Lin],
Heng, P.A.[Pheng-Ann],
Gong, W.[Wei],
Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed
Recognition,
CVPR23(3499-3509)
IEEE DOI
2309
BibRef
Fu, S.[Siming],
Chu, H.P.[Huan-Peng],
He, X.X.[Xiao-Xuan],
Wang, H.L.[Hua-Liang],
Yang, Z.Y.[Zhen-Yu],
Hu, H.J.[Hao-Ji],
Meta-prototype Decoupled Training for Long-tailed Learning,
ACCV22(VI:252-268).
Springer DOI
2307
BibRef
Xu, W.C.[Wei-Chen],
Cao, J.[Jian],
Fu, T.H.[Tian-Hao],
Yao, H.Y.[Hong-Yi],
Wang, Y.[Yuan],
Boosting Dense Long-tailed Object Detection from Data-centric View,
ACCV22(III:558-574).
Springer DOI
2307
BibRef
Penarrubia, C.[Carlos],
Valero-Mas, J.J.[Jose J.],
Gallego, A.J.[Antonio Javier],
Calvo-Zaragoza, J.[Jorge],
Addressing Class Imbalance in Multilabel Prototype Generation for
k-nearest Neighbor Classification,
IbPRIA23(15-27).
Springer DOI
2307
BibRef
Jaiswal, A.[Ajay],
Chen, T.L.[Tian-Long],
Rousseau, J.F.[Justin F.],
Peng, Y.F.[Yi-Fan],
Ding, Y.[Ying],
Wang, Z.Y.[Zhang-Yang],
Attend Who is Weak: Pruning-assisted Medical Image Localization under
Sophisticated and Implicit Imbalances,
WACV23(4976-4985)
IEEE DOI
2302
Location awareness, Training, Pathology, Image color analysis,
Neural networks, Training data, Skin, Biomedical/healthcare/medicine
BibRef
Peng, H.Y.[Han-Yu],
Pian, W.G.[Wei-Guo],
Sun, M.M.[Ming-Ming],
Li, P.[Ping],
Dynamic Re-weighting for Long-tailed Semi-supervised Learning,
WACV23(6453-6463)
IEEE DOI
2302
Training, Uncertainty, Annotations, Semisupervised learning,
Task analysis, Algorithms: Machine learning architectures, visual reasoning
BibRef
Park, C.[Changhwa],
Yim, J.[Junho],
Jun, E.[Eunji],
Mutual Learning for Long-Tailed Recognition,
WACV23(2674-2683)
IEEE DOI
2302
Training, Deep learning, Image recognition, Neural networks, Tail,
Benchmark testing, Algorithms: Machine learning architectures, visual reasoning
BibRef
Lazarow, J.[Justin],
Sohn, K.[Kihyuk],
Lee, C.Y.[Chen-Yu],
Li, C.L.[Chun-Liang],
Zhang, Z.Z.[Zi-Zhao],
Pfister, T.[Tomas],
Unifying Distribution Alignment as a Loss for Imbalanced
Semi-supervised Learning,
WACV23(5633-5642)
IEEE DOI
2302
Training, Codes, Supervised learning, Semisupervised learning,
Entropy, Algorithms: Machine learning architectures,
visual reasoning
BibRef
Nagy, G.[George],
Krishnamoorthy, M.[Mukkai],
One-Against-All Halfplane Dichotomies,
SSSPR22(183-192).
Springer DOI
2301
BibRef
And:
MeFirst ranking and multiple dichotomies:
Via Linear Programming and Neural Networks,
ICPR22(550-556)
IEEE DOI
2212
Training, Sufficient conditions, Neural networks, Urban areas,
Linear programming, Probabilistic logic, unbalanced classes
BibRef
Ye, C.[Changkun],
Barnes, N.M.[Nick M.],
Petersson, L.[Lars],
Tsuchida, R.[Russell],
Efficient Gaussian Process Model on Class-Imbalanced Datasets for
Generalized Zero-Shot Learning,
ICPR22(2078-2085)
IEEE DOI
2212
Training, Prototypes, Training data, Gaussian processes,
Artificial neural networks, Predictive models, Data models
BibRef
Wei, Z.[Zhen],
Zhang, L.[Li],
Zhao, L.[Lei],
DSPOTE: Density-induced Selection Probability-based Oversampling
TEchnique for Imbalanced Learning,
ICPR22(1-7)
IEEE DOI
2212
Filtering, Probability, Noise measurement, Task analysis
BibRef
Riera, C.B.[Carlos Boned],
Terrades, O.R.[Oriol Ramos],
Discriminative Neural Variational Model for Unbalanced Classification
Tasks in Knowledge Graph,
ICPR22(2186-2191)
IEEE DOI
2212
Measurement, Couplings, Semantics, Ear, Benchmark testing, Data models
BibRef
Zhang, Y.[Yupei],
Zhou, Y.[Yaya],
Liu, S.H.[Shu-Hui],
Zhang, W.X.[Wen-Xin],
Xiao, M.[Min],
Shang, X.Q.[Xue-Qun],
WeStcoin: Weakly-Supervised Contextualized Text Classification with
Imbalance and Noisy Labels,
ICPR22(2451-2457)
IEEE DOI
2212
Sensitivity, Costs, Codes, Text categorization, Bit error rate,
Probability
BibRef
Shao, Y.G.[Yang-Guang],
Sun, Y.Y.[Ying-Ying],
Guan, H.J.[Hong-Jiao],
Dual Self-Paced SMOTE for Imbalanced Data,
ICPR22(3083-3089)
IEEE DOI
2212
Training, Sensitivity, Graphical models,
Distribution functions
BibRef
Liu, B.[Bo],
Li, H.X.[Hao-Xiang],
Kang, H.[Hao],
Hua, G.[Gang],
Vasconcelos, N.M.[Nuno M.],
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed
Recognition,
ECCV22(XXIV:637-653).
Springer DOI
2211
BibRef
Tang, K.H.[Kai-Hua],
Tao, M.Y.[Ming-Yuan],
Qi, J.X.[Jia-Xin],
Liu, Z.G.[Zhen-Guang],
Zhang, H.W.[Han-Wang],
Invariant Feature Learning for Generalized Long-Tailed Classification,
ECCV22(XXIV:709-726).
Springer DOI
2211
BibRef
Hong, Y.[Yan],
Zhang, J.[Jianfu],
Sun, Z.Y.[Zhong-Yi],
Yan, K.[Ke],
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image
Classification,
ECCV22(XXIV:587-603).
Springer DOI
2211
BibRef
Wang, H.L.[Hua-Liang],
Fu, S.M.[Si-Ming],
He, X.X.[Xiao-Xuan],
Fang, H.X.[Hang-Xiang],
Liu, Z.Z.[Zuo-Zhu],
Hu, H.J.[Hao-Ji],
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap
Coefficient for Long-Tailed Learning,
ECCV22(XXIV:179-196).
Springer DOI
2211
BibRef
Cho, J.H.[Jang Hyun],
Krähenbühl, P.[Philipp],
Long-tail Detection with Effective Class-Margins,
ECCV22(VIII:698-714).
Springer DOI
2211
BibRef
Dam, T.[Tanmoy],
Ferdaus, M.M.[Md Meftahul],
Pratama, M.[Mahardhika],
Anavatti, S.G.[Sreenatha G.],
Jayavelu, S.[Senthilnath],
Abbass, H.[Hussein],
Latent Preserving Generative Adversarial Network for Imbalance
Classification,
ICIP22(3712-3716)
IEEE DOI
2211
Costs, Codes, Fault detection, Games,
Generative adversarial networks, Generators, class imbalance,
oversampling techniques
BibRef
Escudero-Viñolo, M.[Marcos],
López-Cifuentes, A.[Alejandro],
CCL: Class-Wise Curriculum Learning for Class Imbalance Problems,
ICIP22(1476-1480)
IEEE DOI
2211
Training, Codes, Computational modeling, Data models,
Complexity theory, Class imbalance, Curriculum learning, Image Classification
BibRef
Rangwani, H.[Harsh],
Jaswani, N.[Naman],
Karmali, T.[Tejan],
Jampani, V.[Varun],
Babu, R.V.[R. Venkatesh],
Improving GANs for Long-Tailed Data Through Group Spectral
Regularization,
ECCV22(XV:426-442).
Springer DOI
2211
BibRef
Jiang, C.M.[Chiyu Max],
Najibi, M.[Mahyar],
Qi, C.R.[Charles R.],
Zhou, Y.[Yin],
Anguelov, D.[Dragomir],
Improving the Intra-class Long-Tail in 3D Detection via Rare Example
Mining,
ECCV22(X:158-175).
Springer DOI
2211
BibRef
Yang, Y.Z.[Yu-Zhe],
Wang, H.[Hao],
Katabi, D.[Dina],
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain
Generalization and Beyond,
ECCV22(XX:57-75).
Springer DOI
2211
BibRef
Gu, X.[Xiao],
Guo, Y.[Yao],
Li, Z.[Zeju],
Qiu, J.N.[Jia-Ning],
Dou, Q.[Qi],
Liu, Y.X.[Yu-Xuan],
Lo, B.[Benny],
Yang, G.Z.[Guang-Zhong],
Tackling Long-Tailed Category Distribution Under Domain Shifts,
ECCV22(XXIII:727-743).
Springer DOI
2211
BibRef
Tian, C.Y.[Chang-Yao],
Wang, W.H.[Wen-Hai],
Zhu, X.Z.[Xi-Zhou],
Dai, J.F.[Ji-Feng],
Qiao, Y.[Yu],
VL-LTR: Learning Class-wise Visual-Linguistic Representation for
Long-Tailed Visual Recognition,
ECCV22(XXV:73-91).
Springer DOI
2211
BibRef
Zhang, J.[Jie],
Zhang, L.[Lei],
Li, G.[Gang],
Wu, C.[Chao],
Adversarial Examples for Good:
Adversarial Examples Guided Imbalanced Learning,
ICIP22(136-140)
IEEE DOI
2211
Training, Machine learning, Benchmark testing,
adversarial examples, long-tail data, imbalanced learning
BibRef
Yi, X.Y.[Xuan-Yu],
Tang, K.[Kaihua],
Hua, X.S.[Xian-Sheng],
Lim, J.H.[Joo-Hwee],
Zhang, H.W.[Han-Wang],
Identifying Hard Noise in Long-Tailed Sample Distribution,
ECCV22(XXVI:739-756).
Springer DOI
2211
BibRef
Xu, Y.[Yue],
Li, Y.L.[Yong-Lu],
Li, J.F.[Jie-Feng],
Lu, C.[Cewu],
Constructing Balance from Imbalance for Long-Tailed Image Recognition,
ECCV22(XX:38-56).
Springer DOI
2211
BibRef
Ahmadzadeh, A.[Azim],
Angryk, R.A.[Rafal A.],
Measuring Class-Imbalance Sensitivity of Deterministic Performance
Evaluation Metrics,
ICIP22(51-55)
IEEE DOI
2211
Performance evaluation, Sensitivity, Machine learning,
Behavioral sciences, Task analysis, class imbalance, ROC
BibRef
Zhao, Z.[Zhen],
Zhou, L.P.[Lu-Ping],
Duan, Y.[Yue],
Wang, L.[Lei],
Qi, L.[Lei],
Shi, Y.H.[Ying-Huan],
DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised
Learning,
CVPR22(9747-9755)
IEEE DOI
2210
Training, Degradation, Bridges, Machine learning,
Semisupervised learning, Benchmark testing, Machine learning
BibRef
Park, S.[Seulki],
Hong, Y.[Youngkyu],
Heo, B.[Byeongho],
Yun, S.[Sangdoo],
Choi, J.Y.[Jin Young],
The Majority Can Help the Minority: Context-rich Minority
Oversampling for Long-tailed Classification,
CVPR22(6877-6886)
IEEE DOI
2210
Codes, Benchmark testing,
Classification algorithms, retrieval
BibRef
Alshammari, S.[Shaden],
Wang, Y.X.[Yu-Xiong],
Ramanan, D.[Deva],
Kong, S.[Shu],
Long-Tailed Recognition via Weight Balancing,
CVPR22(6887-6897)
IEEE DOI
2210
Training, Art, Benchmark testing, Data models,
Tuning, Transfer/low-shot/long-tail learning
BibRef
Li, M.[Mengke],
Cheung, Y.M.[Yiu-Ming],
Lu, Y.[Yang],
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment,
CVPR22(6919-6928)
IEEE DOI
2210
Training, Visualization, Graphical models, Perturbation methods,
Neural networks, Tail, Benchmark testing, Transfer/low-shot/long-tail learning
BibRef
Parisot, S.[Sarah],
Esperança, P.M.[Pedro M.],
McDonagh, S.[Steven],
Madarasz, T.J.[Tamas J.],
Yang, Y.X.[Yong-Xin],
Li, Z.G.[Zhen-Guo],
Long-tail Recognition via Compositional Knowledge Transfer,
CVPR22(6929-6938)
IEEE DOI
2210
Training, Analytical models, Prototypes, Tail, Performance gain,
Benchmark testing, Transfer/low-shot/long-tail learning
BibRef
Long, A.[Alexander],
Yin, W.[Wei],
Ajanthan, T.[Thalaiyasingam],
Nguyen, V.[Vu],
Purkait, P.[Pulak],
Garg, R.[Ravi],
Blair, A.[Alan],
Shen, C.H.[Chun-Hua],
van den Hengel, A.J.[Anton J.],
Retrieval Augmented Classification for Long-Tail Visual Recognition,
CVPR22(6949-6959)
IEEE DOI
2210
Training, Visualization, Pipelines, Memory management, Tail,
Transfer/low-shot/long-tail learning,
retrieval
BibRef
Li, B.[Bolian],
Han, Z.[Zongbo],
Li, H.[Haining],
Fu, H.Z.[Hua-Zhu],
Zhang, C.Q.[Chang-Qing],
Trustworthy Long-Tailed Classification,
CVPR22(6960-6969)
IEEE DOI
2210
Measurement, Ethics, Evidence theory, Estimation,
Distributed databases, Tail, Machine learning,
privacy and ethics in vision
BibRef
Wang, T.[Tong],
Zhu, Y.[Yousong],
Chen, Y.Y.[Ying-Ying],
Zhao, C.Y.[Chao-Yang],
Yu, B.[Bin],
Wang, J.Q.[Jin-Qiao],
Tang, M.[Ming],
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object
Detection,
CVPR22(6970-6979)
IEEE DOI
2210
Adaptation models, Sensitivity, Object detection, Detectors, Tail,
Transfer/low-shot/long-tail learning,
retrieval
BibRef
Oh, Y.[Youngtaek],
Kim, D.J.[Dong-Jin],
Kweon, I.S.[In So],
DASO: Distribution-Aware Semantics-Oriented Pseudo-label for
Imbalanced Semi-Supervised Learning,
CVPR22(9776-9786)
IEEE DOI
2210
Semantics, Prototypes, Semisupervised learning, Benchmark testing,
Reliability,
Transfer/low-shot/long-tail learning
BibRef
Fan, Y.[Yue],
Dai, D.X.[Deng-Xin],
Kukleva, A.[Anna],
Schiele, B.[Bernt],
CoSSL: Co-Learning of Representation and Classifier for Imbalanced
Semi-Supervised Learning,
CVPR22(14554-14564)
IEEE DOI
2210
Couplings, Protocols, Codes, Semisupervised learning,
Benchmark testing, Distance measurement,
Transfer/low-shot/long-tail learning
BibRef
Yu, S.[Sihao],
Guo, J.F.[Jia-Feng],
Zhang, R.Q.[Ru-Qing],
Fan, Y.X.[Yi-Xing],
Wang, Z.Z.[Zi-Zhen],
Cheng, X.Q.[Xue-Qi],
A Re-Balancing Strategy for Class-Imbalanced Classification Based on
Instance Difficulty,
CVPR22(70-79)
IEEE DOI
2210
Training, Machine learning algorithms, Heuristic algorithms,
Machine learning, Classification algorithms,
Machine learning
BibRef
Singh, G.[Gursimran],
Chu, L.[Lingyang],
Wang, L.[Lanjun],
Pei, J.[Jian],
Tian, Q.[Qi],
Zhang, Y.[Yong],
Mining Minority-Class Examples with Uncertainty Estimates,
MMMod22(I:258-271).
Springer DOI
2203
BibRef
Zhang, Y.K.[Yi-Kai],
Wang, Q.W.[Qi-Wei],
Zhan, D.C.[De-Chuan],
Ye, H.J.[Han-Jia],
Learning Debiased Representations via Conditional Attribute
Interpolation,
CVPR23(7599-7608)
IEEE DOI
2309
BibRef
Ye, H.J.[Han-Jia],
Zhan, D.C.[De-Chuan],
Chao, W.L.[Wei-Lun],
Procrustean Training for Imbalanced Deep Learning,
ICCV21(92-102)
IEEE DOI
2203
Training, Deep learning, Knowledge engineering, Neural networks,
Fitting, Training data, Recognition and classification,
Transfer/Low-shot/Semi/Unsupervised Learning
BibRef
Park, S.[Seulki],
Lim, J.[Jongin],
Jeon, Y.[Younghan],
Choi, J.Y.[Jin Young],
Influence-Balanced Loss for Imbalanced Visual Classification,
ICCV21(715-724)
IEEE DOI
2203
Training, Learning systems, Visualization, Codes, Benchmark testing,
Data models, Recognition and classification,
Vision applications and systems
BibRef
Wang, Z.[Zhenyi],
Duan, T.[Tiehang],
Fang, L.[Le],
Suo, Q.[Qiuling],
Gao, M.C.[Ming-Chen],
Meta Learning on a Sequence of Imbalanced Domains with Difficulty
Awareness,
ICCV21(8927-8937)
IEEE DOI
2203
Training, Machine learning algorithms, Memory management,
Machine learning, Benchmark testing, Sampling methods,
Recognition and classification
BibRef
Kang, H.Y.[Hae-Yong],
Vu, T.[Thang],
Yoo, C.D.[Chang D.],
Learning Imbalanced Datasets With Maximum Margin Loss,
ICIP21(1269-1273)
IEEE DOI
2201
Training, Schedules, Image processing, Predictive models,
Prediction algorithms, Data models, Maximum Margin (MM) Loss,
Label-Distribution-Aware Margin(LDAM)
BibRef
Okerinde, A.[Ademola],
Hsu, W.[William],
Theis, T.[Tom],
Nafi, N.[Nasik],
Shamir, L.[Lior],
eGAN: Unsupervised Approach to Class Imbalance Using Transfer Learning,
CAIP21(I:322-331).
Springer DOI
2112
BibRef
Wei, C.[Chen],
Sohn, K.[Kihyuk],
Mellina, C.[Clayton],
Yuille, A.L.[Alan L.],
Yang, F.[Fan],
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced
Semi-Supervised Learning,
CVPR21(10852-10861)
IEEE DOI
2111
Adaptation models, Codes, Semisupervised learning
BibRef
Choi, J.W.[Jong-Won],
Yi, K.M.[Kwang Moo],
Kim, J.[Jihoon],
Choo, J.H.[Jin-Ho],
Kim, B.J.[Byoung-Jip],
Chang, J.[Jinyeop],
Gwon, Y.J.[Young-June],
Chang, H.J.[Hyung Jin],
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational
Bayes for Active Learning,
CVPR21(6745-6754)
IEEE DOI
2111
Training, Learning systems, Estimation,
Object detection, Task analysis
BibRef
Wang, J.F.[Jian-Feng],
Lukasiewicz, T.[Thomas],
Hu, X.L.[Xiao-Lin],
Cai, J.F.[Jian-Fei],
Xu, Z.H.[Zheng-Hua],
RSG: A Simple but Effective Module for Learning Imbalanced Datasets,
CVPR21(3783-3792)
IEEE DOI
2111
Training, Deep learning, Codes, Generators,
Convolutional neural networks
BibRef
Sharma, S.[Saurabh],
Yu, N.[Ning],
Fritz, M.[Mario],
Schiele, B.[Bernt],
Long-Tailed Recognition Using Class-Balanced Experts,
GCPR20(86-100).
Springer DOI
2110
BibRef
Duarte, K.[Kevin],
Rawat, Y.[Yogesh],
Shah, M.[Mubarak],
PLM: Partial Label Masking for Imbalanced Multi-label Classification,
LLID21(2733-2742)
IEEE DOI
2109
Training, Neural networks,
Linear programming, Classification algorithms
BibRef
He, C.[Chen],
Wang, R.P.[Rui-Ping],
Chen, X.L.[Xi-Lin],
A Tale of Two CILs: The Connections between Class Incremental
Learning and Class Imbalanced Learning, and Beyond,
CLVision21(3554-3564)
IEEE DOI
2109
Learning systems, Collaboration
BibRef
Patashnik, O.[Or],
Danon, D.[Dov],
Zhang, H.[Hao],
Cohen-Or, D.[Daniel],
BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains,
LLID21(2653-2661)
IEEE DOI
2109
Training, Image quality,
Task analysis
BibRef
Yu, W.P.[Wei-Ping],
Yang, T.[Taojiannan],
Chen, C.[Chen],
Towards Resolving the Challenge of Long-tail Distribution in UAV
Images for Object Detection,
WACV21(3257-3266)
IEEE DOI
2106
Head, Image resolution,
Computational modeling, Object detection, Detectors
BibRef
Kim, B.[Byungju],
Hong, H.G.[Hyeong Gwon],
Kim, J.[Junmo],
De-biasing Neural Networks with Estimated Offset for Class Imbalanced
Learning,
WACV21(1478-1486)
IEEE DOI
2106
Training, Neural networks, Training data, Benchmark testing
BibRef
Tripathi, A.[Ayush],
Chakraborty, R.[Rupayan],
Kopparapu, S.I.K.[Sun-Il Kumar],
A Novel Adaptive Minority Oversampling Technique for Improved
Classification in Data Imbalanced Scenarios,
ICPR21(10650-10657)
IEEE DOI
2105
Training, Measurement, Machine learning algorithms,
Clustering algorithms, Machine learning, Partitioning algorithms,
Minority class
BibRef
Kocaman, V.[Veysel],
Shir, O.M.[Ofer M.],
Bäck, T.[Thomas],
The Unreasonable Effectiveness of the Final Batch Normalization Layer,
ISVC21(II:81-93).
Springer DOI
2112
BibRef
And:
Improving Model Accuracy for Imbalanced Image Classification Tasks by
Adding a Final Batch Normalization Layer: An Empirical Study,
ICPR21(10404-10411)
IEEE DOI
2105
Training, Visualization, Uncertainty, Measurement uncertainty,
Transfer learning, Pipelines, Predictive models
BibRef
Aggarwal, U.[Umang],
Popescu, A.[Adrian],
Hudelot, C.[Céline],
Minority Class Oriented Active Learning for Imbalanced Datasets,
ICPR21(9920-9927)
IEEE DOI
2105
Training, Learning systems, Image color analysis, Annotations,
Transfer learning, Performance gain
BibRef
Beltrán, L.V.B.[L. Viviana Beltrán],
Coustaty, M.[Mickaël],
Journet, N.[Nicholas],
Caicedo, J.C.[Juan C.],
Doucet, A.[Antoine],
Multi-Attribute Learning With Highly Imbalanced Data,
ICPR21(9219-9226)
IEEE DOI
2105
Deep learning, Location awareness, Adaptation models, Databases,
Optimized production technology, Feature extraction, Data models
BibRef
Sicilia, R.[Rosa],
Cordelli, E.[Ermanno],
Soda, P.[Paolo],
Categorizing the feature space for two-class imbalance learning,
ICPR21(6181-6188)
IEEE DOI
2105
Training, Degradation, Reliability engineering,
Classification algorithms, Proposals, Indexes,
Features space
BibRef
Li, Y.G.[Yong-Gang],
Zhou, Y.F.[Ya-Feng],
Wang, Y.T.[Yong-Tao],
Qin, X.R.[Xiao-Ran],
Tang, Z.[Zhi],
Dual Loss for Manga Character Recognition with Imbalanced Training
Data,
ICPR21(2166-2171)
IEEE DOI
2105
Training, Measurement, Adaptation models, Fitting, Training data,
Benchmark testing, Data models
BibRef
Zhu, H.[Hao],
Yuan, Y.[Yang],
Hu, G.S.[Guo-Sheng],
Wu, X.[Xiang],
Robertson, N.[Neil],
Imbalance Robust Softmax for Deep Embeeding Learning,
ACCV20(V:274-291).
Springer DOI
2103
BibRef
Huang, H.[He],
Saito, S.[Shunta],
Kikuchi, Y.[Yuta],
Matsumoto, E.[Eiichi],
Tang, W.[Wei],
Yu, P.S.[Philip S.],
Addressing Class Imbalance in Scene Graph Parsing by Learning to
Contrast and Score,
ACCV20(VI:461-477).
Springer DOI
2103
BibRef
Dutta, T.[Titir],
Singh, A.[Anurag],
Biswas, S.[Soma],
Adaptive Margin Diversity Regularizer for Handling Data Imbalance in
Zero-Shot SBIR,
ECCV20(V:349-364).
Springer DOI
2011
BibRef
Hu, X.T.[Xin-Ting],
Jiang, Y.[Yi],
Tang, K.H.[Kai-Hua],
Chen, J.Y.[Jing-Yuan],
Miao, C.Y.[Chun-Yan],
Zhang, H.W.[Han-Wang],
Learning to Segment the Tail,
CVPR20(14042-14051)
IEEE DOI
2008
Training, Head, Visualization, Task analysis, Image segmentation,
Data models, Cats
BibRef
Zhou, B.,
Cui, Q.,
Wei, X.,
Chen, Z.,
BBN: Bilateral-Branch Network With Cumulative Learning for
Long-Tailed Visual Recognition,
CVPR20(9716-9725)
IEEE DOI
2008
Training, Error analysis, Feature extraction, Data models,
Visualization, Benchmark testing
BibRef
Zhu, L.C.[Lin-Chao],
Yang, Y.[Yi],
Inflated Episodic Memory With Region Self-Attention for Long-Tailed
Visual Recognition,
CVPR20(4343-4352)
IEEE DOI
2008
Visualization, Prototypes, Training, Feature extraction, Robustness,
Data models, Encoding
BibRef
Li, Y.,
Wang, T.,
Kang, B.,
Tang, S.,
Wang, C.,
Li, J.,
Feng, J.,
Overcoming Classifier Imbalance for Long-Tail Object Detection With
Balanced Group Softmax,
CVPR20(10988-10997)
IEEE DOI
2008
Training, Object detection, Proposals, Adaptation models,
Feature extraction, Computational modeling, Detectors
BibRef
Kim, J.,
Jeong, J.,
Shin, J.,
M2m: Imbalanced Classification via Major-to-Minor Translation,
CVPR20(13893-13902)
IEEE DOI
2008
Training, Machine-to-machine communications, Neural networks,
Standards, Testing, Art
BibRef
Aggarwal, U.,
Popescu, A.,
Hudelot, C.,
Active Learning for Imbalanced Datasets,
WACV20(1417-1426)
IEEE DOI
2006
Labeling, Machine learning, Manuals, Uncertainty, Predictive models,
Entropy, Adaptation models
BibRef
Wang, T.,
Zhao, J.,
Yatskar, M.,
Chang, K.,
Ordonez, V.,
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender
Bias in Deep Image Representations,
ICCV19(5309-5318)
IEEE DOI
2004
gender issues, image representation,
learning (artificial intelligence), neural nets, Neural networks
BibRef
Wang, Y.,
Gan, W.,
Yang, J.,
Wu, W.,
Yan, J.,
Dynamic Curriculum Learning for Imbalanced Data Classification,
ICCV19(5016-5025)
IEEE DOI
2004
learning (artificial intelligence), pattern classification, Data models
BibRef
Hayat, M.,
Khan, S.,
Zamir, S.W.,
Shen, J.,
Shao, L.,
Gaussian Affinity for Max-Margin Class Imbalanced Learning,
ICCV19(6468-6478)
IEEE DOI
2004
feature extraction, Gaussian processes, image classification,
learning (artificial intelligence), pattern clustering, Neural networks
BibRef
Khan, S.[Salman],
Hayat, M.[Munawar],
Zamir, S.W.[Syed Waqas],
Shen, J.B.[Jian-Bing],
Shao, L.[Ling],
Striking the Right Balance With Uncertainty,
CVPR19(103-112).
IEEE DOI
2002
BibRef
Kim, B.[Byungju],
Kim, H.W.[Hyun-Woo],
Kim, K.[Kyungsu],
Kim, S.[Sungjin],
Kim, J.[Junmo],
Learning Not to Learn: Training Deep Neural Networks With Biased Data,
CVPR19(9004-9012).
IEEE DOI
2002
BibRef
Anantrasirichai, N.,
Bull, D.,
Defectnet: Multi-Class Fault Detection on Highly-Imbalanced Datasets,
ICIP19(2481-2485)
IEEE DOI
1910
convolutional neural network, segmentation, detection, classification
BibRef
Langenkämper, D.[Daniel],
van Kevelaer, R.[Robin],
Nattkemper, T.W.[Tim W.],
Strategies for Tackling the Class Imbalance Problem in Marine Image
Classification,
CVAUI18(26-36).
Springer DOI
1901
BibRef
Liang, P.,
Yuan, X.,
Li, W.,
Hu, J.,
A Segmented Local Offset Method for Imbalanced Data Classification
Using Quasi-Linear Support Vector Machine,
ICPR18(746-751)
IEEE DOI
1812
Support vector machines, Partitioning algorithms,
Classification algorithms, Complexity theory,
Training data
BibRef
Sarafianos, N.[Nikolaos],
Xu, X.[Xiang],
Kakadiaris, I.A.[Ioannis A.],
Deep Imbalanced Attribute Classification Using Visual Attention
Aggregation,
ECCV18(XI: 708-725).
Springer DOI
1810
BibRef
Nguyen, T.T.T.,
Liew, A.W.C.,
Nguyen, T.T.,
Wang, S.,
A Novel Bayesian Framework for Online Imbalanced Learning,
DICTA17(1-7)
IEEE DOI
1804
Bayes methods, data handling, geometry,
learning (artificial intelligence), matrix algebra,
Training
BibRef
Sze-To, A.[Antonio],
Wong, A.K.C.[Andrew K. C.],
A Weight-Selection Strategy on Training Deep Neural Networks for
Imbalanced Classification,
ICIAR17(3-10).
Springer DOI
1706
BibRef
Soleymani, R.,
Granger, E.,
Fumera, G.,
Loss factors for learning Boosting ensembles from imbalanced data,
ICPR16(204-209)
IEEE DOI
1705
Boosting, Error analysis, Measurement,
Standards, Training
BibRef
Guan, H.J.[Hong-Jiao],
Zhang, Y.T.[Ying-Tao],
Xian, M.[Min],
Cheng, H.D.,
Tang, X.L.[Xiang-Long],
WENN for individualized cleaning in imbalanced data,
ICPR16(456-461)
IEEE DOI
1705
Cleaning, Noise measurement, Robustness, Sensitivity, Shape, Training,
WENN, data cleaning, imbalanced, data
BibRef
Tax, D.M.J.,
Wang, F.,
Class-dependent, non-convex losses to optimize precision,
ICPR16(3314-3319)
IEEE DOI
1705
Labeling, Logistics, Neural networks, Optimization, Robustness,
Standards, Training, Imbalanced classes,
Multiple Instance Learning, Positive and Unlabeled data,
Supervised learning, non-convex, optimization
BibRef
Huang, C.,
Li, Y.,
Loy, C.C.,
Tang, X.,
Learning Deep Representation for Imbalanced Classification,
CVPR16(5375-5384)
IEEE DOI
1612
BibRef
Rong, T.,
Tian, X.,
Ng, W.W.Y.,
Location bagging-based undersampling for imbalanced classification
problems,
ICWAPR16(72-77)
IEEE DOI
1611
Pattern recognition
BibRef
Alejo, R.[Roberto],
Monroy-de-Jesús, J.[Juan],
Pacheco-Sánchez, J.H.[J. Horacio],
Valdovinos, R.M.[Rosa María],
Antonio-Velázquez, J.A.[Juan A.],
Marcial-Romero, J.R.[J. Raymundo],
Analysing the Safe, Average and Border Samples on Two-Class Imbalance
Problems in the Back-Propagation Domain,
CIARP15(699-707).
Springer DOI
1511
BibRef
Mera, C.[Carlos],
Arrieta, J.[Jose],
Orozco-Alzate, M.[Mauricio],
Branch, J.[John],
A Bag Oversampling Approach for Class Imbalance in Multiple Instance
Learning,
CIARP15(724-731).
Springer DOI
1511
BibRef
Mera, C.[Carlos],
Orozco-Alzate, M.[Mauricio],
Branch, J.[John],
Improving Representation of the Positive Class in Imbalanced
Multiple-Instance Learning,
ICIAR14(I: 266-273).
Springer DOI
1410
BibRef
García, V.,
Sánchez, J.S.,
Ochoa-Domínguez, H.J.,
Cleofas-Sánchez, L.,
Dissimilarity-Based Learning from Imbalanced Data with Small Disjuncts
and Noise,
IbPRIA15(370-378).
Springer DOI
1506
BibRef
Famili, A.F.[A. Fazel],
Searching for Patterns in Imbalanced Data,
CIARP14(159-166).
Springer DOI
1411
BibRef
Kockentiedt, S.[Stephen],
Tönnies, K.[Klaus],
Gierke, E.[Erhardt],
Predicting the Influence of Additional Training Data on Classification
Performance for Imbalanced Data,
GCPR14(377-387).
Springer DOI
1411
BibRef
Sandhan, T.[Tushar],
Choi, J.Y.[Jin Young],
Handling Imbalanced Datasets by Partially Guided Hybrid Sampling for
Pattern Recognition,
ICPR14(1449-1453)
IEEE DOI
1412
Databases
BibRef
Giraldo-Forero, A.F.[Andrés Felipe],
Jaramillo-Garzón, J.A.[Jorge Alberto],
Ruiz-Muñoz, J.F.[José Francisco],
Managing Imbalanced Data Sets in Multi-label Problems:
A Case Study with the SMOTE Algorithm,
CIARP13(I:334-342).
Springer DOI
1311
BibRef
Hernandez, J.[Julio],
Carrasco-Ochoa, J.A.[Jesús Ariel],
Martínez-Trinidad, J.F.[José Francisco],
An Empirical Study of Oversampling and Undersampling for Instance
Selection Methods on Imbalance Datasets,
CIARP13(I:262-269).
Springer DOI
1311
See also New Method for Skeleton Pruning, A.
See also Prototype Selection for Graph Embedding Using Instance Selection.
BibRef
Song, Y.,
Morency, L.P.,
Davis, R.,
Distribution-sensitive learning for imbalanced datasets,
FG13(1-6)
IEEE DOI
1309
data analysis. Datasets imbalanced across classes (faces, gestures)
BibRef
Alejo, R.,
Toribio, P.,
Valdovinos, R.M.,
Pacheco-Sanchez, J.H.,
A Modified Back-Propagation Algorithm to Deal with Severe Two-Class
Imbalance Problems on Neural Networks,
MCPR12(265-272).
Springer DOI
1208
BibRef
González-Barcenas, V.M.,
Rendón, E.,
Alejo, R.,
Granda-Gutiérrez, E.E.,
Valdovinos, R.M.,
Addressing the Big Data Multi-class Imbalance Problem with Oversampling
and Deep Learning Neural Networks,
IbPRIA19(I:216-224).
Springer DOI
1910
BibRef
Alejo, R.,
Martínez Sotoca, J.[José],
Casañ, G.A.,
An Empirical Study for the Multi-class Imbalance Problem with Neural
Networks,
CIARP08(479-486).
Springer DOI
0809
BibRef
Utasi, A.[Akos],
Weighted conditional mutual information based boosting for
classification of imbalanced datasets,
ICPR12(2711-2714).
WWW Link.
1302
BibRef
d'Ambrosio, R.[Roberto],
Iannello, G.[Giulio],
Soda, P.[Paolo],
A One-per-Class reconstruction rule for class imbalance learning,
ICPR12(1310-1313).
WWW Link.
1302
BibRef
d'Ambrosio, R.[Roberto],
Soda, P.[Paolo],
Polichotomies on Imbalanced Domains by One-per-Class Compensated
Reconstruction Rule,
SSSPR12(301-309).
Springer DOI
1211
One of more classes underrepresented in training.
BibRef
Millán-Giraldo, M.[Mónica],
García, V.[Vicente],
Sánchez, J.S.[J. Salvador],
One-Sided Prototype Selection on Class Imbalanced Dissimilarity
Matrices,
SSSPR12(391-399).
Springer DOI
1211
BibRef
García, V.[Vicente],
Sánchez, J.S.[Javier Salvador],
Mollineda, R.A.[Ramón A.],
Classification of High Dimensional and Imbalanced Hyperspectral Imagery
Data,
IbPRIA11(644-651).
Springer DOI
1106
BibRef
Earlier: A1, A3, A2:
Theoretical Analysis of a Performance Measure for Imbalanced Data,
ICPR10(617-620).
IEEE DOI
1008
BibRef
Earlier: A1, A3, A2:
Index of Balanced Accuracy: A Performance Measure for Skewed Class
Distributions,
IbPRIA09(441-448).
Springer DOI
0906
BibRef
And: A1, A3, A2:
A New Performance Evaluation Method for Two-Class Imbalanced Problems,
SSPR08(917-925).
Springer DOI
0812
BibRef
Earlier: A1, A2, A3:
An Empirical Study of the Behavior of Classifiers on Imbalanced and
Overlapped Data Sets,
CIARP07(397-406).
Springer DOI
0711
BibRef
García, V.,
Mollineda, R.A.,
Sánchez, J.S.,
Alejo, R.,
Martínez Sotoca, J.[José],
When Overlapping Unexpectedly Alters the Class Imbalance Effects,
IbPRIA07(II: 499-506).
Springer DOI
0706
BibRef
Ghanem, A.S.[Amal S.],
Venkatesh, S.[Svetha],
West, G.A.W.[Geoff A.W.],
Multi-class Pattern Classification in Imbalanced Data,
ICPR10(2881-2884).
IEEE DOI
1008
BibRef
Earlier:
Learning in imbalanced relational data,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Yin, D.W.[Da-Wei],
An, C.[Chang],
Baird, H.S.[Henry S.],
Imbalance and Concentration in k-NN Classification,
ICPR10(2170-2173).
IEEE DOI
1008
BibRef
Nguyen, G.H.[Giang H.],
Bouzerdoum, A.[Abdesselam],
Phung, S.L.[Son L.],
A supervised learning approach for imbalanced data sets,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Molinara, M.,
Ricamato, M.T.,
Tortorella, F.,
Facing Imbalanced Classes through Aggregation of Classifiers,
CIAP07(43-48).
IEEE DOI
0709
BibRef
Cheng, H.T.[Hsien-Ting],
Chen, C.S.[Chu-Song],
A Complementary Ordering Method for Class Imbalanced Problem,
ICPR06(III: 429-432).
IEEE DOI
0609
Asymmetric Bagging with Vector Complementary Ordering.
Apply to biometrics.
BibRef
Cantador, I.[Iván],
Dorronsoro, J.R.[José R.],
Parallel Perceptrons, Activation Margins and Imbalanced Training Set
Pruning,
IbPRIA05(II:43).
Springer DOI
0509
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Transfer Learning from Other Tasks, Other Classes .