Abstract
Fuzzy Support Vector Machines (FSVMs) based on spherical regions are proposed in this paper. Firstly, the center of the spherite is determined by all the training data. Secondly, the membership functions are defined with the distances between each data and the center of the spherite. Thirdly, using the suitable parameter λ, FSVMs are formed on the spherical regions. One-against-one decision strategy of FSVMs is adopted so that the proposed FSVMs can be extended to solve multi-class problems. In order to verify the superiority of the proposed FSVMs, the traditional two-class and multi-class problems of machine learning benchmark datasets are used to test the feasibility and performance of the proposed FSVMs. The experiment results indicate that the new approach not only has higher precision but also downsizes the number of training data and reduces the running time.
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