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J. Wavelets Multiresolut Inf. Process."],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:p> Positive instances are often significantly less than negative instances in real-world classification problems. However, positive categories are typically more relevant to the primary focus of categorization tasks. Moreover, obtaining labeled data is often expensive, and the majority of real-life data is unlabeled. Therefore, semi-supervised learning has become a popular approach for addressing imbalanced problems. Traditional support vector machines (SVMs) treat all samples equally and are not suitable for semi-supervised learning. To address this issue, a semi-supervised model called the fuzzy semi-supervised SVM (FS<jats:sup>3<\/jats:sup>VM) has been proposed. The FS<jats:sup>3<\/jats:sup>VM model uses the degree of entropy-based fuzzy membership to ensure the materiality of positive classes by assigning positive instances to relatively large degrees of fuzzy membership. After introducing the mainstream FS<jats:sup>3<\/jats:sup>VM model, the fundamental theory and methods of the model are discussed and expanded upon, including the FS<jats:sup>3<\/jats:sup>VM algorithm, which applies the Sequential Minimal Optimization (SMO) algorithm to the dual problem. The proposed FS<jats:sup>3<\/jats:sup>VM model is a smooth and continuous optimization problem, and its dual is a standard quadratic programming. Experimental results demonstrate that the proposed FS<jats:sup>3<\/jats:sup>VM model outperforms other compared learning algorithms. <\/jats:p>","DOI":"10.1142\/s0219691323500388","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T16:19:21Z","timestamp":1690561161000},"source":"Crossref","is-referenced-by-count":1,"title":["A classification method of fuzzy semi-supervised support vector machines for the problems of imbalance"],"prefix":"10.1142","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7999-2190","authenticated-orcid":false,"given":"Jing","family":"Quan","sequence":"first","affiliation":[{"name":"School of Science, Chongqing University of Technology, Chongqing, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengli","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Science, Chongqing University of Technology, Chongqing, P. R. 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