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Erschienen in:
2013 | OriginalPaper | Buchkapitel
67. Fuzzy SVM with a New Fuzzy Membership Function Based on Gray Relational Grade
verfasst von : Wan Mei Tang, Yi Zhang, Ping Wang
Erschienen in: Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012
Verlag: Springer London
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Abstract
In dealing with the two-class classification problems, the traditional support vector machine (SVM) often cannot achieve good classification accuracy when outliers exist in the training data set. The fuzzy support vector machine (FSVM) can resolve this problem with an appropriate fuzzy membership for each data point. The effect of the outliers can be effectively reduced when the classification problem is solved. In this paper, gray relational analysis (GRA) is employed to search for gray relational grade (GRG) which can be used to describe the relationships between the data attributes and to determine the important samples that significantly influence some defined objectives. A new fuzzy membership function for the FSVM is calculated based on the GRG. This method can distinguish the support vectors and the outliers effectively. Experimental results show that this approach contributes greatly to the reduction of the effect of the outliers and significantly improves the classification accuracy and generalization.