Open AccessDual Membership Fuzzy Support Vector Machine Algorithm Based on SVDD
Author(s) -
Yingcheng Xu,
Wei Feng,
Fei Pei,
Haiyan Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1624/4/042021
Subject(s) - support vector machine , dual (grammatical number) , sample (material) , pattern recognition (psychology) , set (abstract data type) , data set , relevance vector machine , artificial intelligence , algorithm , computer science , fuzzy logic , data mining , fuzzy set , structured support vector machine , mathematics , literature , art , chemistry , chromatography , programming language
In the case of excessive overlap between positive and negative samples in data set, the deviation in the category of reconstructed sample points will lead to unsatisfactory discrimination of SVM, no matter what methods are used to reconstruct the sample set. A dual membership fuzzy support vector machine algorithm based on support vector data domain description was thus proposed, followed by a simulation analysis of common data set. Experimental results show that the proposed algorithm can work well in classification when the sample set is overlapped.