Feature Selection Based on Fuzzy SVM

The feature selection consists of obtaining a subset of these features to optimally realize the task without the irrelevant ones. Since it can provide faster and cost-effective learning machine and also improve the prediction performance of the predictors, it is a crucial step in machine learning. The feature selection methods using support machine have obtained satisfactory results, but the noises and outliers often reduce the performance. In this paper, we propose a feature selection approach based on nonlinear fuzzy support vector machine, in which the fuzzy membership is calculated in the feature space and is represented by kernels. This method gives good performance on reducing the effects of outliers and improves the results of feature selection.