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Support vector classification with fuzzy hyperplane

In this paper, we develop a novel support vector algorithm with fuzzy hyperplane for pattern classification. We first introduce the concepts of fuzzy hyperplane and fuzzy linear separability. Then, the proposed approach seeks a fuzzy hyperplane that best separates the positive class from the negative class with the widest margin in the feature space. Further, the decision function of the proposed approach is generalized so the values assigned to the individuals fall within a specified range and indicate the membership degree of these individuals in a given category. This integration preserves the benefits of fuzzy set theory and SVM theory, where the use of the fuzzy hyperplane provides the SVM with effective means for capturing the approximate, imprecise nature of the real world. On the other hand, the SVM provides the advantage to minimize the structural risk and effectively generalize the unseen data. Experimental results are then presented which indicate the performance of the proposed approach.