{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/oversampling-for-imbalanced-learning-based-on","title":"Oversampling for Imbalanced Learning Based on K-Means and SMOTE","arxiv_id":"1711.00837","date":"2017-11-02","proceeding":null,"authors":["Felix Last","Georgios Douzas","Fernando Bacao"],"abstract":"Learning from class-imbalanced data continues to be a common and challenging\nproblem in supervised learning as standard classification algorithms are\ndesigned to handle balanced class distributions. While different strategies\nexist to tackle this problem, methods which generate artificial data to achieve\na balanced class distribution are more versatile than modifications to the\nclassification algorithm. Such techniques, called oversamplers, modify the\ntraining data, allowing any classifier to be used with class-imbalanced\ndatasets. Many algorithms have been proposed for this task, but most are\ncomplex and tend to generate unnecessary noise. This work presents a simple and\neffective oversampling method based on k-means clustering and SMOTE\noversampling, which avoids the generation of noise and effectively overcomes\nimbalances between and within classes. Empirical results of extensive\nexperiments with 71 datasets show that training data oversampled with the\nproposed method improves classification results. Moreover, k-means SMOTE\nconsistently outperforms other popular oversampling methods. An implementation\nis made available in the python programming language.","url_abs":"http://arxiv.org/abs/1711.00837v2","url_pdf":"http://arxiv.org/pdf/1711.00837v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"oversampling-for-imbalanced-learning-based-on","repo_url":"https://github.com/felix-last/kmeans_smote","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"data-to-text-generation","task_name":"Data-to-Text Generation"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[{"method_slug":"k-means-clustering","method_name":"k-Means Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.00837","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}