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But although the classification speed and performance of TSVM is better than that of primitive support vector machine, TSVM still faces the problem of difficult parameter selection; therefore, to overcome the problem of parameter selection of TSVM, this paper proposes a Chaotic Mapping Dung Beetle Optimization Algorithm-based Twin Support Vector Machine (CMDBO-TSVM) for automatic parameter selection. Due to the uncertainty of the random initialization population of the original Dung Beetle Optimization Algorithm, this paper additionally adds chaotic mapping initialization to improve the Dung Beetle Optimization Algorithm. Experiments on the dataset through this paper show that the classification accuracy of the CMDBO-TSVM has a better performance.<\/jats:p>","DOI":"10.1093\/jcde\/qwae040","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T03:44:30Z","timestamp":1713843870000},"page":"101-110","source":"Crossref","is-referenced-by-count":9,"title":["Twin support vector machines based on chaotic mapping dung beetle optimization algorithm"],"prefix":"10.1093","volume":"11","author":[{"given":"Huajuan","family":"Huang","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Guangxi Minzu University , Nanning 530006 ,\u00a0 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenhua","family":"Yao","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Guangxi Minzu University , Nanning 530006 ,\u00a0 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