Fuzzy multi-class classifier based on support vector data description and improved PCM

Authors:
Yong Zhang;Zhong-Xian Chi;Ke-Qiu Li
Affiliations:
Department of Computer, Liaoning Normal University, Dalian 116081, China and Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, China;Department of Computer Science and Engineering, Dalian University of Technology, Dalian 116024, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

In this paper, a novel fuzzy classifier for multi-classification problems, based on support vector data description (SVDD) and improved PCM, is proposed. The proposed method is the robust version of SVDD by assigning a weight to each data point, which represents fuzzy membership degree of the cluster computed by the improved PCM method. Accordingly, this paper presents the multi-classification algorithm based on the robust weighted SVDD, and gives the simple classification rule. Experimental results show that the proposed method can reduce the effect of outliers and yield higher classification rate.