Feature Subset Selection and Classification Using Hybrid Improved Svm

Many feature subset selection algorithms have been proposed, but not all of them are appropriate for a given feature selection problem. At the same time, so far there is rarely a good way to choose appropriate feature subset selection algorithms for the problem at hand. Feature selection has become an essential element in the Data Mining process. In this paper, investigate the problem of efficient feature selection for classification on High Dimensional datasets. present a feature reduction method for overcome a loss of accuracy of classification after that perform a classification process with the aid of modified fuzzy c-means clustering with rough set theory it is used to perform a feature selection process. Once the feature reduction is formed, the classification will be done based on the Hybrid kernel based Improved SVM (ISVM) classifier. In this classification, the optimal kernel is identified using Grey Wolf Optimization (GWO).