14.1.5.2 Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data

Chapter Contents (Back)
Imbalanced Data. Unbalanced Data. Learning.
See also Long Tailed Data Analysis.
See also Long-Tailed Object Detection.
See also Unbalanced Data, Oversample or Undersample Solutions.

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

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

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

Liu, R.C.[Ruo-Chen], Chen, Y.Y.[Yang-Yang], Jiao, L.C.[Li-Cheng], Li, Y.Y.[Yang-Yang],
A particle swarm optimization based simultaneous learning framework for clustering and classification,
PR(47), No. 6, 2014, pp. 2143-2152.
Elsevier DOI 1403
Classification 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

Jiang, Z.L.[Zhuo-Lin], Lin, Z.[Zhe], Ling, H.B.[Hai-Bin], Porikli, F.M.[Fatih M.], Shao, L.[Ling], Turaga, P.K.[Pavan K.],
Discriminative feature learning from big data for visual recognition,
PR(48), No. 10, 2015, pp. 2961-2963.
Elsevier DOI 1507
BibRef

Zhao, J.[Ji], Wang, L.T.[Lian-Tao], Cabral, R.S., de la Torre, F.,
Feature and Region Selection for Visual Learning,
IP(25), No. 3, March 2016, pp. 1084-1094.
IEEE DOI 1602
image processing 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

Hafiz, F.[Faizal], Swain, A.[Akshya], Patel, N.[Nitish], Naik, C.[Chirag],
A two-dimensional (2-D) learning framework for Particle Swarm based feature selection,
PR(76), No. 1, 2018, pp. 416-433.
Elsevier DOI 1801
Classification BibRef

Huang, J., Li, G., Huang, Q., Wu, X.,
Joint Feature Selection and Classification for Multilabel Learning,
Cyber(48), No. 3, March 2018, pp. 876-889.
IEEE DOI 1802
Algorithm design and analysis, Computers, Control engineering, Correlation, Feature extraction, Prediction algorithms, Transforms, shared features 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

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

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

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

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

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

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

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

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

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

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

Huang, R.[Rui], Jiang, W.D.[Wei-Dong], Sun, G.L.[Guang-Ling],
Manifold-based constraint Laplacian score for multi-label feature selection,
PRL(112), 2018, pp. 346-352.
Elsevier DOI 1809
BibRef

Huang, R.[Rui], Wu, Z.J.[Zhe-Jun],
Multi-label feature selection via manifold regularization and dependence maximization,
PR(120), 2021, pp. 108149.
Elsevier DOI 2109
Multi-label learning, Feature selection, Sparse regression, Manifold regularization, Dependence maximization 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

Hao, J.Y.[Jing-Yu], Wang, C.J.[Cheng-Jia], Yang, G.[Guang], Gao, Z.F.[Zhi-Fan], 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

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

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

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

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

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

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

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

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

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

Deng, Y.X.[Yu-Xin], Ma, J.Y.[Jia-Yi],
ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning,
IP(32), 2023, pp. 591-602.
IEEE DOI 2301
Feature extraction, Detectors, Training, Reliability, Representation learning, Optimization, Benchmark testing, image matching 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

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

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

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

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

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

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

Deng, H.W.[Han-Wen], Zhang, W.J.[Wei-Jia], Zhang, M.L.[Min-Ling],
Instance-dependent label noise learning via separating style from content,
PRL(196), 2025, pp. 9-15.
Elsevier DOI 2509
Instance-dependent label noise, Weakly-supervised learning, Causal representation 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

Miftahushudur, T.[Tajul], Sahin, H.M.[Halil Mertkan], Grieve, B.[Bruce], Yin, H.J.[Hu-Jun],
A Survey of Methods for Addressing Imbalance Data Problems in Agriculture Applications,
RS(17), No. 3, 2025, pp. 454.
DOI Link 2502
BibRef

Wu, F.[Feiyan], Liu, Z.[Zhunga], Zhang, Z.[Zuowei], Liu, J.X.[Jia-Xiang], Wang, L.F.[Long-Fei],
Collaborative Global-Local Structure Network With Knowledge Distillation for Imbalanced Data Classification,
CirSysVideo(35), No. 3, March 2025, pp. 2450-2460.
IEEE DOI 2503
Data models, Knowledge engineering, Tail, Data augmentation, Representation learning, Training, Reviews, Federated learning, imbalanced data classification BibRef

Ma, Y.B.[Yan-Biao], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Wen, M.[Maoji], Li, L.L.[Ling-Ling], Ma, W.P.[Wen-Ping], Yang, S.Y.[Shu-Yuan], Liu, X.[Xu], Chen, P.[Puhua],
Predicting and Enhancing the Fairness of DNNs With the Curvature of Perceptual Manifolds,
PAMI(47), No. 5, May 2025, pp. 3394-3411.
IEEE DOI 2504
Manifolds, Accuracy, Artificial neural networks, Tail, Heavily-tailed distribution, Correlation, Data models, data-centirc AI BibRef

Ning, Z.H.[Zhi-Han], Guo, C.[Chaoxun], Zhang, D.[David],
Meta-distribution-based ensemble sampler for imbalanced semi-supervised learning,
PR(164), 2025, pp. 111552.
Elsevier DOI Code:
WWW Link. 2504
Semi-supervised learning, Imbalanced data, Ensemble learning, Data resampling, Histogram discretization BibRef

Guo, E.[Erjian], Wang, Z.C.[Zi-Cheng], Zhao, Z.[Zhen], Zhou, L.P.[Lu-Ping],
Imbalanced Medical Image Segmentation With Pixel-Dependent Noisy Labels,
MedImg(44), No. 5, May 2025, pp. 2016-2027.
IEEE DOI Code:
WWW Link. 2505
Noise measurement, Noise, Image segmentation, Biomedical imaging, Training, Federated learning, Data models, Annotations, learning with label noise BibRef

Ye-Bin, M.[Moon], Hyeon-Woo, N.[Nam], Choi, W.[Wonseok], Kim, N.[Nayeong], Kwak, S.[Suha], Oh, T.H.[Tae-Hyun],
SYNAuG: Exploiting synthetic data for data imbalance problems,
PRL(193), 2025, pp. 115-121.
Elsevier DOI 2505
Data augmentation, Synthetic data long-tailed recognition, Fairness, Model robustness BibRef

Ding, H.W.[Hong-Wei], Huang, N.[Nana], Wu, Y.X.[Yao-Xin], Cui, X.H.[Xiao-Hui],
Improving imbalanced medical image classification through GAN-based data augmentation methods,
PR(166), 2025, pp. 111680.
Elsevier DOI 2505
Imbalanced data, Generative adversarial networks, Intra-class mode collapse, Data augmentation BibRef

Naik, S.M.[Shraddha M.], Chakraborty, T.[Tanujit], Panja, M.[Madhurima], Hadid, A.[Abdenour], Chakraborty, B.[Bibhas],
Skew-probabilistic neural networks for learning from imbalanced data,
PR(165), 2025, pp. 111677.
Elsevier DOI 2505
Imbalanced classification, Probabilistic neural networks, Skew-normal distribution, Bat algorithm, Consistency BibRef

Yang, A.[Aijia], Zhang, M.[Min], Chen, H.[Huai], Li, T.[Taihao], Liu, S.[Shupeng], Xu, X.Y.[Xiao-Yin],
tanh As a robust feature scaling method in training deep learning models with imbalanced data,
PR(167), 2025, pp. 111746.
Elsevier DOI 2506
Feature scaling, z-score standardization, tanh, Deep learning, Imbalanced data, Gradient explosion BibRef

Chen, P.[Pengdi], Liu, Y.[Yong], Ren, Y.[Yuanrui], Zhang, B.[Baoan], Zhao, Y.[Yuan],
A Deep Learning-Based Solution to the Class Imbalance Problem in High-Resolution Land Cover Classification,
RS(17), No. 11, 2025, pp. 1845.
DOI Link 2506
BibRef

Hou, Z.[Zhi], Yu, B.[Baosheng], Wang, C.[Chaoyue], Zhan, Y.B.[Yi-Bing], Tao, D.C.[Da-Cheng],
Learning to Explore Sample Relationships,
PAMI(47), No. 7, July 2025, pp. 5445-5459.
IEEE DOI 2506
Transformers, Training, Representation learning, Zero shot learning, Heavily-tailed distribution, sample relationship BibRef

Huang, Z.[Zihan], Tian, P.Y.[Peng-Yu], Zhu, H.[Hao], Guo, P.[Pute], Li, X.T.[Xiao-Tong],
A Dual-Branch Network for Intra-Class Diversity Extraction in Panchromatic and Multispectral Classification,
RS(17), No. 12, 2025, pp. 1998.
DOI Link 2506
BibRef

Zhao, Y.[Yudi], Hao, K.R.[Kuang-Rong], Gu, C.C.[Chao-Chen], Wei, B.[Bing], Guan, X.P.[Xin-Ping],
Distribution Learning Based on Evolutionary Algorithm-Assisted Deep Neural Networks for Imbalanced Image Classification,
Cyber(55), No. 8, August 2025, pp. 3723-3736.
IEEE DOI 2508
Image classification, Feature extraction, Evolutionary computation, Training, Covariance matrices, quality and diversity BibRef

Valero-Mas, J.J.[Jose J.], Penarrubia, C.[Carlos], Castellanos, F.J.[Francisco J.], Gallego, A.J.[Antonio Javier], Calvo-Zaragoza, J.[Jorge],
Insights into imbalance-aware Multilabel Prototype Generation mechanisms for k-Nearest Neighbor classification in noisy scenarios,
PR(169), 2026, pp. 111884.
Elsevier DOI 2509
Multilabel learning, Imbalanced classification, Noisy labels, Prototype Generation, Efficient -Nearest Neighbor BibRef

Lin, Z.[Zhikai], Xu, Y.[Yong], Liu, K.[Kunhong], Chen, L.Y.[Li-Yan],
MDGP-forest: A novel deep forest for multi-class imbalanced learning based on multi-class disassembly and feature construction enhanced by genetic programming,
PR(170), 2026, pp. 112070.
Elsevier DOI 2509
Multiclass imbalance learning, Deep forest, Feature construction, Instance hardness, Genetic Programming BibRef

McKenney, M.[Mark], Tucek, D.[Daniel],
Statistical Depth Measures in Density-Based Clustering with Automatic Adjustment for Skewed Data,
IJGI(14), No. 8, 2025, pp. 298.
DOI Link 2509
BibRef

Rong, X.[Xinhui], Solo, V.[Victor],
Asymptotic Classification Error for Heavy-Tailed Renewal Processes,
SPLetters(32), 2025, pp. 3769-3773.
IEEE DOI 2510
Error probability, Heavily-tailed distribution, Hazards, Monte Carlo methods, Upper bound, Trajectory, Training, Laplace transform BibRef

Wang, Y.[Yuan], Chang, Y.K.[Ya-Kun], Qin, Y.[Ying], Zhao, Y.[Yao], Wei, S.[Shikui],
Unbiased Sample Selection and Label Improvement for Mitigating Noisy Labels in Class-Imbalanced Datasets,
CirSysVideo(35), No. 10, October 2025, pp. 10070-10082.
IEEE DOI 2510
Noise measurement, Training, Noise, Tail, Reliability, Accuracy, Semisupervised learning, Data models, Dogs, Data mining, pseudo-label generation BibRef

Qezelbash-Chamak, J.[Jaber], Hicklin, K.[Karen], Kim, M.[Minhee],
KANBalance: Kolmogorov-Arnold network mitigates class imbalance,
PR(171), 2026, pp. 112325.
Elsevier DOI 2511
Deep learning, Kolmogorov-Arnold networks, Focal loss, Class imbalance, Biomedical informatics BibRef

Li, M.Y.[Meng-Yang], Zhou, X.L.[Xiao-Ling], Wu, O.[Ou],
Delving Into the Training Dynamics for Image Classification,
IP(34), 2025, pp. 6783-6798.
IEEE DOI Code:
WWW Link. 2511
Training, Noise measurement, Time series analysis, Image classification, Representation learning, imbalance learning BibRef

Quan, Y.[Yu], Zhang, D.[Dong], Tang, J.H.[Jin-Hui],
Generalized Concordant Vision Transformer With Masked Image Tokens for Object Detection,
CirSysVideo(35), No. 11, November 2025, pp. 10616-10631.
IEEE DOI 2511
Object detection, Training, Detectors, Semantics, Transformers, Correlation, Feature extraction, class imbalanced learning BibRef

Dong, Z.J.[Zi-Jian], Wu, Y.L.[Yi-Lei], Chen, C.Y.[Chong-Yao], Zou, Y.[Yingtian], Zhang, Y.[Yichi], Zhou, J.H.[Juan Helen],
Improve Representation for Imbalanced Regression through Geometric Constraints,
CVPR25(5082-5091)
IEEE DOI 2508
Representation learning, Codes, Computational modeling, Data visualization, Benchmark testing, Data models, Trajectory, imbalanced regression BibRef

Chen, W.X.[Wen-Xi], Yeh, R.A.[Raymond A.], Mou, S.S.[Shao-Shuai], Gu, Y.[Yan],
Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection,
CVPR25(4724-4733)
IEEE DOI 2508
Training, Deep learning, Bridges, Perturbation methods, Computational modeling, Training data, Robustness, adversarial attack BibRef

Ling, Z.W.[Zhi-Wei], Chang, Y.[Yachen], Zhao, H.L.[Hai-Liang], Zhao, X.[Xinkui], Chow, K.[Kingsum], Deng, S.[Shuiguang],
CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging,
CVPR25(4968-4977)
IEEE DOI 2508
Measurement, Buildings, Artificial neural networks, Benchmark testing, Feature extraction, Robustness, out-of-distribution detection BibRef

Tang, K.[Keke], Hou, C.[Chao], Peng, W.L.[Wei-Long], Fang, X.[Xiang], Wu, Z.[Zhize], Nie, Y.W.[Yong-Wei], Wang, W.P.[Wen-Ping], Tian, Z.H.[Zhi-Hong],
Simplification Is All You Need against Out-of-Distribution Overconfidence,
CVPR25(5030-5040)
IEEE DOI 2508
Buildings, Artificial neural networks, out-of-distribution, overconfidence, deep neural networks BibRef

Song, M.Y.[Ming-Yang], Qu, X.Y.[Xiao-Ye], Zhou, J.W.[Jia-Wei], Cheng, Y.[Yu],
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration,
CVPR25(9434-9444)
IEEE DOI 2508
Visualization, Adaptation models, Head, Heavily-tailed distribution, Training data, lvlms BibRef

Li, Y.C.[Yu-Chuan], Kang, J.M.[Jae-Mo], Kim, I.M.[Il-Min],
Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data,
CVPR25(10183-10192)
IEEE DOI 2508
Training, Adaptation models, Accuracy, Training data, Estimation, Predictive models, Data models, Iterative methods, Reliability, deep learning algorithm BibRef

Li, S.[Shawn], Gong, H.X.[Hui-Xian], Dong, H.[Hao], Yang, T.[Tiankai], Tu, Z.Z.[Zheng-Zhong], Zhao, Y.[Yue],
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution Detection,
CVPR25(10193-10202)
IEEE DOI Code:
WWW Link. 2508
Training, Prototypes, Machine learning, Predictive models, Prediction algorithms, Robustness, Reproducibility of results, Videos BibRef

Mildenberger, D.[David], Hager, P.[Paul], Rueckert, D.[Daniel], Menten, M.J.[Martin J.],
A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets,
CVPR25(10305-10314)
IEEE DOI 2508
Measurement, Heavily-tailed distribution, Codes, Accuracy, Contrastive learning, Medical diagnosis, Standards, binary imbalanced datasets BibRef

Zhang, X.X.[Xing-Xuan], Li, J.S.[Jian-Sheng], Chu, W.J.[Wen-Jing], Hai, J.J.[Jun-Jia], Xu, R.Z.[Ren-Zhe], Yang, Y.Q.[Yu-Qing], Guan, S.[Shikai], Xu, J.Z.[Jia-Zheng], Jing, L.P.[Li-Ping], Cui, P.[Peng],
On the Out-Of-Distribution Generalization of Large Multimodal Models,
CVPR25(10315-10326)
IEEE DOI 2508
Training, Visualization, Correlation, Limiting, Semantics, Feature extraction, Robustness, Biomedical imaging, generalization BibRef

Xu, Z.[Zhuo], Xiang, X.[Xiang], Liang, Y.F.[Yi-Fan],
Overcoming Shortcut Problem in VLM for Robust Out-of-Distribution Detection,
CVPR25(15402-15412)
IEEE DOI Code:
WWW Link. 2508
Couplings, Codes, Semantics, Interference, Robustness, vision-language models, out-of-distribution detection BibRef

Liu, Y.H.[Yu-Hang], Zhao, W.J.[Wen-Jie], Guo, Y.H.[Yun-Hui],
H2ST: Hierarchical Two-Sample Tests for Continual Out-of-Distribution Detection,
CVPR25(15413-15423)
IEEE DOI Code:
WWW Link. 2508
Incremental learning, Codes, Computational modeling, Predictive models, Data models, Testing BibRef

Liu, L.[Litian], Qin, Y.[Yao],
Detecting Out-of-distribution through the Lens of Neural Collapse,
CVPR25(15424-15433)
IEEE DOI Code:
WWW Link. 2508
Computational modeling, Detectors, Feature extraction, Market research, Vectors, Robustness, Lenses, Convergence BibRef

Wallin, E.[Erik], Kahl, F.[Fredrik], Hammarstrand, L.[Lars],
ProHOC: Probabilistic Hierarchical Out-of-Distribution Classification via Multi-Depth Networks,
CVPR25(20612-20621)
IEEE DOI Code:
WWW Link. 2508
Deep learning, Codes, Semantics, Probabilistic logic, Standards, out-of-distribution detection, class hierarchy BibRef

Tian, Z.C.[Zi-Chen], Liu, Y.Y.[Yao-Yao], Sun, Q.[Qianru],
Meta-Learning Hyperparameters for Parameter Efficient Fine-Tuning,
CVPR25(23037-23047)
IEEE DOI 2508
Metalearning, Training, Heavily-tailed distribution, Transfer learning, Modulation, Manuals, Tuning, Remote sensing, remote-sensing BibRef

Smadar, Y.[Yair], Hoogi, A.[Assaf],
Dynamic Group Normalization: Spatio-Temporal Adaptation to Evolving Data Statistics,
CVPR25(30167-30177)
IEEE DOI 2508
Training, Heavily-tailed distribution, Accuracy, Artificial neural networks, Benchmark testing, Robustness, statistical awareness BibRef

Li, J.L.[Jin-Long], Bao, L.Q.[Li-Qun], Zhang, W.Z.[Wei-Zhao],
Imbalanced data classification method based on an improved fruit fly optimization algorithm and Adaboost,
ICIVC24(210-214)
IEEE DOI 2503
Costs, Accuracy, Heuristic algorithms, Computational modeling, Classification algorithms, Optimization, imbalanced data, Adaboost 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

Park, T.[Taemin], Lee, H.[Hyuck], Kim, H.[Heeyoung],
Rebalancing Using Estimated Class Distribution for Imbalanced Semi-supervised Learning Under Class Distribution Mismatch,
ECCV24(XXII: 388-404).
Springer DOI 2412
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

Ye, C.K.[Chang-Kun], 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

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

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

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

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.K.[Chang-Kun], 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

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.P.[Yu-Pei], 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

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

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

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

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.Y.[Ling-Yang], 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

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

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

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

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

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

Shen, W., Li, F., Liu, R.,
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks,
SDL-CV19(733-737)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, learning (artificial intelligence), Information flow 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

Liu, L.J.[Li-Juan], Bao, Y.[Yu], Li, H.J.[Hao-Jie], Fan, X.[Xin], Luo, Z.X.[Zhong-Xuan],
Discriminative Feature Learning with an Optimal Pattern Model for Image Classification,
MMMod16(I: 675-685).
Springer DOI 1601
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