Optimizing support vector machines for multi-class classification

Abstract

The accuracy obtained when classifying multi-class data depends on the classifier and the features used for training the classifier. The parameters passed to the classifier and feature selection techniques can help improve accuracy. In this paper we propose certain dataset and classifier optimization to help improve the accuracy when classifying multi-class data. These optimization also help in reducing the training time.