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SMOTE is a famous oversampling method of imbalanced learning. However, it has some disadvantages such as sample overlapping, noise interference, and blindness of neighbor selection. In order to address these problems, we present a new oversampling method, OS-CCD, based on a new concept, the classification contribution degree. The classification contribution degree determines the number of synthetic samples generated by SMOTE for each positive sample. OS-CCD follows the spatial distribution characteristics of original samples on the class boundary, as well as avoids oversampling from noisy points. Experiments on twelve benchmark datasets demonstrate that OS-CCD outperforms six classical oversampling methods in terms of accuracy, F1-score, AUC, and ROC.<\/jats:p>","DOI":"10.3390\/sym13020194","type":"journal-article","created":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T12:03:57Z","timestamp":1611662637000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["A New Oversampling Method Based on the Classification Contribution Degree"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhenhao","family":"Jiang","sequence":"first","affiliation":[{"name":"DUT-BSU Joint Institute, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingting","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.neucom.2019.06.100","article-title":"Smote-variants: A python implementation of 85 minority oversampling techniques","volume":"366","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105662","DOI":"10.1016\/j.asoc.2019.105662","article-title":"An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets","volume":"83","year":"2019","journal-title":"Appl. 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