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imbalanced-learn-extra: A Python Package for Novel Oversampling Algorithms Cover

imbalanced-learn-extra: A Python Package for Novel Oversampling Algorithms

Open Access
|Jan 2026

References

  1. Barua S, Islam MM, Yao X, Murase K. MWMOTE–Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning. IEEE Transactions on Knowledge and Data Engineering. 2014;26(2):40525. DOI: 10.1109/TKDE.2012.232
  2. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic Minority Over-Sampling Technique. Journal of Artificial Intelligence Research. 2002;16(June):32157. DOI: 10.1613/jair.953
  3. Chawla NV, Lazarevic A, Hall LO, Bowyer KW. SMOTEBoost: Improving Prediction of the Minority Class in Boosting. In: Goos G, Hartmanis J, van Leeuwen J, Lavrač N, Gamberger D, Todorovski L, Blockeel H, editors. Knowledge Discovery in Databases: PKDD 2003, vol. 2838. Berlin, Heidelberg: Springer Berlin Heidelberg; 2003. pp. 10719. DOI: 10.1007/978-3-540-39804-2_12
  4. Douzas G, Bacao F. Self-Organizing Map Oversampling (SOMO) for Imbalanced Data Set Learning. Expert Systems with Applications. 2017;82(October):4052. DOI: 10.1016/j.eswa.2017.03.073
  5. Douzas G, Bacao F. Geometric SMOTE a Geometrically Enhanced Drop-in Replacement for SMOTE. Information Sciences. 2019;501(October):11835. DOI: 10.1016/j.ins.2019.06.007
  6. Douzas G, Bacao F, Last F. Improving Imbalanced Learning Through a Heuristic Oversampling Method Based on k-Means and SMOTE. Information Sciences. 2018;465(October):120. DOI: 10.1016/j.ins.2018.06.056
  7. Douzas G, Rauch R, Bacao F. G-SOMO: An Oversampling Approach Based on Self-Organized Maps and Geometric SMOTE. Expert Systems with Applications. 2021;183(November):115230. DOI: 10.1016/j.eswa.2021.115230
  8. Fernández A, López V, Galar M, Del Jesus MJ, Herrera F. Analysing the Classification of Imbalanced Data-Sets with Multiple Classes: Binarization Techniques and Ad-Hoc Approaches. Knowledge-Based Systems. 2013;42(April):97110. DOI: 10.1016/j.knosys.2013.01.018
  9. He H, Garcia EA. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering. 2009;21(9):126384. DOI: 10.1109/TKDE.2008.239
  10. Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G. Learning from Class-Imbalanced Data: Review of Methods and Applications. Expert Systems with Applications. 2017;73(May):22039. DOI: 10.1016/j.eswa.2016.12.035
  11. Lemaitre G, Nogueira F, Aridas CK. Imbalanced -Learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. 2016. DOI: 10.48550/ARXIV.1609.06570
  12. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. Scikit-Learn: Machine Learning in Python. 2012. DOI: 10.48550/ARXIV.1201.0490
  13. Prati RC, Batista GEAPA, Monard MC. Learning with Class Skews and Small Disjuncts. In: Hutchison D, Kanade T, Kittler J, Kleinberg JM, Mattern F, Mitchell JC, Naor M, et al., editors. Advances in Artificial Intelligence – SBIA 2004, vol. 3171. Berlin, Heidelberg: Springer Berlin Heidelberg; 2004. pp. 296306. DOI: 10.1007/978-3-540-28645-5_30
DOI: https://doi.org/10.5334/jors.459 | Journal eISSN: 2049-9647
Language: English
Submitted on: Feb 26, 2023
Accepted on: Nov 24, 2025
Published on: Jan 19, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Georgios Douzas, Fernando Bacao, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.