A new fuzzy membership computation method for fuzzy support vector machines

Support vector machine (SVM) considers all data points with the same importance in classification problems, therefore SVM is very sensitive to noisy data or outliers. Current fuzzy approach to two-class SVM introduces a fuzzy membership to each data point in order to reduce the sensitivity of less important data, however computing fuzzy memberships is still a challenge. It has been found that the performance of fuzzy SVM highly depends on the computation of fuzzy memberships, hence in this paper, we propose a new method to compute fuzzy memberships and we also extend the fuzzy approach for two-class SVM to one-class SVM. Experiments performed on a number of popular data sets to evaluation the proposed fuzzy SVMs show promising classification results.