Boundary‐based Fuzzy‐SVDD for one‐class classification

International Journal of Intelligent SystemsVolume 37, Issue 3 p. 2266-2292 RESEARCH ARTICLE Boundary-based Fuzzy-SVDD for one-class classification Dongdong Li, Dongdong Li Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this authorXinlei Xu, Xinlei Xu Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this authorZhe Wang, Corresponding Author Zhe Wang [email protected] orcid.org/0000-0002-3759-2041 Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, China Correspondence Zhe Wang, Department of Computer Science & Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, 200237, China. Email: [email protected]Search for more papers by this authorChenjie Cao, Chenjie Cao Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this authorMinguang Wang, Minguang Wang Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this author Dongdong Li, Dongdong Li Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this authorXinlei Xu, Xinlei Xu Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this authorZhe Wang, Corresponding Author Zhe Wang [email protected] orcid.org/0000-0002-3759-2041 Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, China Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, China Correspondence Zhe Wang, Department of Computer Science & Engineering, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science & Technology, Shanghai, 200237, China. Email: [email protected]Search for more papers by this authorChenjie Cao, Chenjie Cao Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this authorMinguang Wang, Minguang Wang Department of Computer Science & Engineering, East China University of Science & Technology, Shanghai, ChinaSearch for more papers by this author First published: 06 December 2021 https://doi.org/10.1002/int.22773Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Support Vector Data Description (SVDD) is an extremely hot topic issue in One-Class Classification (OCC), which has displayed outstanding performance in dealing with many novelty detection problems. However, SVDD just takes the data description by the kernel-based distance among each instance into consideration rather than considering the distribution of the data. Therefore, Fuzzy Support Vector Data Description (Fuzzy-SVDD) has been developed to distribute a fuzzy membership to each input sample so that different samples cause different contributions to classification boundary. The majority of the methods in Fuzzy-SVDD are based on the sample density, but there are remaining two problems. These density-based Fuzzy-SVDD methods would decrease the contribution of support vectors (SVs) in low densities. What is more, these methods cannot get a precise density when there are few target samples. These two problems would lead to a poor classification boundary. To overcome these drawbacks, a novel method called Boundary-based Fuzzy-SVDD (BF-SVDD) is proposed in this paper. BF-SVDD uses a new definition called local–global center distance to search for the samples near the boundary. Then, it enhances fuzzy memberships of these samples because they carry more significant information for the decision boundary than other data. The contribution of this paper can be summarized into three main points. First a novel concept called local–global center distances is proposed to find the SVs better. Second, fuzzy memberships with local–global center distance make SVs more informative to create the decision boundary. Furthermore, the experiments based on University of California, Irvine and Knowledge Extraction based on Evolutionary Learning also show that the proposed method has excellent performances. Even for the minority class in imbalance data sets, the proposed method can also have a good classification. Volume37, Issue3March 2022Pages 2266-2292 RelatedInformation