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Results show that the proposed method wins over competing methods in classification performance and the ability to find minority classes. Those classifiers based-twin architectures have more advantages than those classifiers based-single architecture in classification ability. We demonstrate that the complexity of imbalanced data distribution has negative effects on classification results, whereas, the advanced classification results and the desired boundaries can be gained by optimizing the kernel.<\/jats:p>","DOI":"10.3233\/jifs-222501","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T11:21:03Z","timestamp":1669720863000},"page":"6901-6910","source":"Crossref","is-referenced-by-count":0,"title":["A novel twin-support vector machines method for binary classification to imbalanced data"],"prefix":"10.1177","volume":"44","author":[{"given":"Jingyi","family":"Li","sequence":"first","affiliation":[{"name":"Chongqing College of Mobile Telecommunications, Chongqing Key Laboratory of Public Big Data Security Technology, Chongqing, China"}]},{"given":"Shiwei","family":"Chao","sequence":"additional","affiliation":[{"name":"Chongqing Jiangbei International Airport Co., Ltd., Chongqing, 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