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Fuzzy multi-class classifier based on support vector data description and improved PCM
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DOI：10.1016/j.eswa.2008.03.026.png)
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Abstract
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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. (c) 2008 Elsevier Ltd. All rights reserved.
Keywords:
Support vector data description
Possibilistic c-means algorithm
Minimum enclosing sphere
Classifier
SVM
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