Performance evaluation of support vector machine classification approaches in data mining

At present, knowledge extraction from the given data set plays a significant role in all the fields in our society. Feature selection process used to choose a few relevant features to achieve better classification performance. The existing feature selection algorithms consider the job as a single objective problem. Selecting attributes is prepared by the combination of attribute evaluation and search method using the WEKA Machine Learning Tool. The proposed method is performed in three phases. In the first step, support vector classifiers are implemented with four different kernel methods such as linear function, Polynomial function, Radial basis function and sigmoid functions to classify data items. In the second step, classifier subset evaluation is applied to feature selection, along with the SVM classification for optimizing feature vectors and this obtains the maximum accuracy. In the third step, introducing new kernel approach which generates the maximum accuracy in classification compared to the other four kernel methods. From the experimental analysis, SVM with the proposed kernel approach has produced maximum accuracy over other kernel methods.