A novel SVM based approach for noisy data elemination

In this paper we propose a novel Support Vector Machine(SVM) based approach for noisy data removal from datasets. It is observed that the instability present in the dataset greatly affects the overall performance of the any classifier. Hence, we propose a methodology for removal of such instabilities. In the proposed approach, we proceed by determining the clusters formed using support equilibrium points. Then analyzing, each cluster and remove the noisy data using the accuracy factor. Our approach, provide an important feature for reducing the training time and reducing the misclassification test error. The methodology if adopted for classifiers before the training phase will enhance the efficiency of the system. The approach is being tested on benchmark dataset, and it is observed that the efficiency of classifier increased by 15-20%.