{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T07:17:31Z","timestamp":1758266251288},"reference-count":0,"publisher":"National Library of Serbia","issue":"3","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:p>Imbalanced datasets exist widely in real life. The identification of the minority class in imbalanced datasets tends to be the focus of classification. As a variant of enhanced support vector machine (SVM), the twin support vector machine (TWSVM) provides an effective technique for data classification. TWSVM is based on a relative balance in the training sample dataset and distribution to improve the classification accuracy of the whole dataset, however, it is not effective in dealing with imbalanced data classification problems. In this paper, we propose to combine a re-sampling technique, which utilizes oversampling and under-sampling to balance the training data, with TWSVM to deal with imbalanced data classification. Experimental results show that our proposed approach outperforms other state-of-art methods.<\/jats:p>","DOI":"10.2298\/csis161221017l","type":"journal-article","created":{"date-parts":[[2017,8,8]],"date-time":"2017-08-08T09:42:32Z","timestamp":1502185352000},"page":"579-595","source":"Crossref","is-referenced-by-count":9,"title":["Imbalanced data classification based on hybrid resampling and twin support vector machine"],"prefix":"10.2298","volume":"14","author":[{"given":"Lu","family":"Cao","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, School of data science and computer science, Guangzhou, China + Wuyi University, School of Information Engineering, Jiangmen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"Shen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, School of data science and computer science, Guangzhou, China + University of Adelaide, School of Computer Science, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","container-title":["Computer Science and Information Systems"],"original-title":[],"language":"en","deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T08:33:06Z","timestamp":1685349186000},"score":1,"resource":{"primary":{"URL":"https:\/\/doiserbia.nb.rs\/Article.aspx?ID=1820-02141700017C"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2017]]}},"URL":"https:\/\/doi.org\/10.2298\/csis161221017l","relation":{},"ISSN":["1820-0214","2406-1018"],"issn-type":[{"value":"1820-0214","type":"print"},{"value":"2406-1018","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]}}}