14.1.5.2.1 Unbalanced Data, Oversample or Undersample Solutions

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

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

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

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

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

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

Zhu, T.F.[Tuan-Fei], Lin, Y.P.[Ya-Ping], Liu, Y.H.[Yong-He],
Synthetic minority oversampling technique for multiclass imbalance problems,
PR(72), No. 1, 2017, pp. 327-340.
Elsevier DOI 1708
Multiclass imbalance problems 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

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

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

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

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

Koziarski, M.[Michal],
Potential Anchoring for imbalanced data classification,
PR(120), 2021, pp. 108114.
Elsevier DOI 2109
Machine learning, Classification, Imbalanced data, Oversampling, Undersampling, Radial basis functions BibRef

Liu, B.[Bin], Blekas, K.[Konstantinos], Tsoumakas, G.[Grigorios],
Multi-label sampling based on local label imbalance,
PR(122), 2022, pp. 108294.
Elsevier DOI 2112
Multi-label learning, Class imbalance, Oversampling and undersampling, Local label imbalance, Ensemble methods BibRef

Maldonado, S.[Sebastián], Vairetti, C.[Carla], Fernandez, A.[Alberto], Herrera, F.[Francisco],
FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification,
PR(124), 2022, pp. 108511.
Elsevier DOI 2203
Data resampling, SMOTE, OWA Operators, Feature selection, Imbalanced data classification BibRef

Sridhar, S., Kalaivani, A.,
Performance Analysis of Two-Stage Iterative Ensemble Method over Random Oversampling Methods on Multiclass Imbalanced Datasets,
IJIG(22), No. 2, April 2022, pp. 2250025.
DOI Link 2205
BibRef

Liu, Y.X.[Yong-Xu], Liu, Y.[Yan], Yu, B.X.B.[Bruce X.B.], Zhong, S.H.[Sheng-Hua], Hu, Z.J.[Zhe-Jing],
Noise-robust oversampling for imbalanced data classification,
PR(133), 2023, pp. 109008.
Elsevier DOI 2210
Imbalanced learning, Classification, Clustering BibRef

Ren, J.J.[Jin-Jun], Wang, Y.P.[Yu-Ping], Cheung, Y.M.[Yiu-Ming], Gao, X.Z.[Xiao-Zhi], Guo, X.F.[Xiao-Fang],
Grouping-based Oversampling in Kernel Space for Imbalanced Data Classification,
PR(133), 2023, pp. 108992.
Elsevier DOI 2210
Imbalanced data classification, Kernel method, Support vector machine, Oversampling BibRef

Liu, C.L.[Chien-Liang], Chang, Y.H.[Yu-Hua],
Learning From Imbalanced Data With Deep Density Hybrid Sampling,
SMCS(52), No. 11, November 2022, pp. 7065-7077.
IEEE DOI 2210
Boosting, Training, Euclidean distance, Sampling methods, Costs, Hybrid power systems, Estimation, Class imbalance, synthetic data BibRef

Datta, D.[Debaleena], Mallick, P.K.[Pradeep Kumar], Reddy, A.V.N.[Annapareddy V. N.], Mohammed, M.A.[Mazin Abed], Jaber, M.M.[Mustafa Musa], Alghawli, A.S.[Abed Saif], Al-Qaness, M.A.A.[Mohammed A. A.],
A Hybrid Classification of Imbalanced Hyperspectral Images Using ADASYN and Enhanced Deep Subsampled Multi-Grained Cascaded Forest,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Soltanzadeh, P.[Paria], Feizi-Derakhshi, M.R.[M. Reza], Hashemzadeh, M.[Mahdi],
Addressing the class-imbalance and class-overlap problems by a metaheuristic-based under-sampling approach,
PR(143), 2023, pp. 109721.
Elsevier DOI 2310
Imbalanced classification, Imbalanced datasets, Class overlap, Class imbalance, Metaheuristic algorithms, Under-sampling BibRef

Schultz, K.[Kristian], Bej, S.[Saptarshi], Hahn, W.[Waldemar], Wolfien, M.[Markus], Srivastava, P.[Prashant], Wolkenhauer, O.[Olaf],
ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets,
PR(147), 2024, pp. 110138.
Elsevier DOI 2312
Imbalanced data, Convex space learning, LoRAS, GAN, Tabular data BibRef

Yu, H.[Hao], Du, Y.X.[Ying-Xiao], Wu, J.X.[Jian-Xin],
Reviving undersampling for long-tailed learning,
PR(161), 2025, pp. 111200.
Elsevier DOI Code:
WWW Link. 2502
Image classification, Long-tailed learning, Undersampling BibRef

Zhao, L.Y.[Ling-Yun], Han, F.[Fei], Ling, Q.H.[Qing-Hua], Ge, Y.[Yubin], Zhang, Y.Z.[Yu-Ze], Liu, Q.[Qing], Han, H.[Henry],
Contribution-based imbalanced hybrid resampling ensemble,
PR(164), 2025, pp. 111553.
Elsevier DOI 2504
Class-imbalanced learning, Hybrid resampling, Sample contribution, Ensemble learning, Semi-supervised Learning BibRef

Wei, Z.[Zhen], Zhang, L.[Li], Zhao, L.[Lei],
DSPOTE: Density-induced Selection Probability-based Oversampling TEchnique for Imbalanced Learning,
ICPR22(1-7)
IEEE DOI 2212
Filtering, Probability, Noise measurement, Task analysis BibRef

Dam, T.[Tanmoy], Ferdaus, M.M.[Md Meftahul], Pratama, M.[Mahardhika], Anavatti, S.G.[Sreenatha G.], Jayavelu, S.[Senthilnath], Abbass, H.[Hussein],
Latent Preserving Generative Adversarial Network for Imbalance Classification,
ICIP22(3712-3716)
IEEE DOI 2211
Costs, Codes, Fault detection, Games, Generative adversarial networks, Generators, class imbalance, oversampling techniques BibRef

Park, S.[Seulki], Hong, Y.[Youngkyu], Heo, B.[Byeongho], Yun, S.[Sangdoo], Choi, J.Y.[Jin Young],
The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification,
CVPR22(6877-6886)
IEEE DOI 2210
Codes, Benchmark testing, Classification algorithms, retrieval BibRef

Tripathi, A.[Ayush], Chakraborty, R.[Rupayan], Kopparapu, S.I.K.[Sun-Il Kumar],
A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios,
ICPR21(10650-10657)
IEEE DOI 2105
Training, Measurement, Machine learning algorithms, Clustering algorithms, Machine learning, Partitioning algorithms, Minority class BibRef

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

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

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

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

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

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Transfer Learning from Other Tasks, Other Classes .