Associating kNN and SVM for Higher Classification Accuracy

Abstract

The paper proposed a hybrid two-stage method of support vector machines (SVM) to increase its performance in classification accuracy. In this model, a filtering stage of the k nearest neighbor (kNN) rule was employed to collect information from training observations and re-evaluate balance weights for the observations based on their influences. The balance weights changed the policy of the discrete class label. A novel idea of real-valued class labels for transferring the balance weights was therefore proposed. Embedded in the class label, the weights given as the penalties of the uncertain outliers in the classification were considered in the quadratic programming of SVM, and produced a different hyperplane with higher accuracy. The adoption of kNN rule in the filtering stage has the advantage to distinguish the uncertain outliers in an independent way. The results showed that the classification accuracy of the hybrid model was higher than that of the classical SVM.