14.1.4.2 Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data, Long-Tailed Data

Chapter Contents (Back)
Imbalanced Data. Unbalanced Data. Learning.

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.[Ziyu], Ma, Z.[Zhanyu], 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.[Yiwen], 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.[Jiahang],
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

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.[Jinhu], 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.[Jinhu], 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


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, Computer vision 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

Peng, J., Bu, X., Sun, M., Zhang, Z., 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

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, Computer vision, Testing, Art BibRef

Wang, X., Lyu, Y., Jing, L.,
Deep Generative Model for Robust Imbalance Classification,
CVPR20(14112-14121)
IEEE DOI 2008
Perturbation methods, Uncertainty, Conferences, Computer vision, Pattern recognition, Data models 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

Liu, J., Du, A., Wang, C., Zheng, H., Wang, N., Zheng, B.,
Teaching Squeeze-and-Excitation PyramidNet for Imbalanced Image Classification with GAN-based Curriculum Learning,
ICPR18(2444-2449)
IEEE DOI 1812
Training, Network architecture, Computer architecture, Task analysis, Measurement 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

Yu, L., Fan, G.,
Edge-aware integration model for semantic labeling of rare classes,
ICIP17(4482-4486)
IEEE DOI 1803
Image color analysis, Image edge detection, Image segmentation, Labeling, Probabilistic logic, Semantics, Training, CNN, Superpixel 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

Cruz, R.[Ricardo], Fernandes, K.[Kelwin], Costa, J.F.P.[Joaquim F. Pinto], Ortiz, M.P.[María Pérez], Cardoso, J.S.[Jaime S.],
Ordinal Class Imbalance with Ranking,
IbPRIA17(3-12).
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, Electronic mail, Error analysis, Measurement, Pattern recognition, Standards, Training BibRef

Guan, H.[Hongjiao], 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.[Dawei], An, C.[Chang], Baird, H.S.[Henry S.],
Imbalance and Concentration in k-NN Classification,
ICPR10(2170-2173).
IEEE DOI 1008
BibRef

Merler, M.[Michele], Yan, R.[Rong], Smith, J.R.[John R.],
Imbalanced RankBoost for efficiently ranking large-scale image/video collections,
CVPR09(2607-2614).
IEEE DOI 0906
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 Classes .


Last update:Nov 19, 2020 at 14:58:59