A Multi-Class SVM Classification System Based on Methods of Self-Learning and Error Filtering

Abstract

In this paper, the technique of Support Vector Machine has been used to deal with multi-class Chinese text classification. Several data retrieving techniques including word segmentation, term weighting and feature extraction are adopted to implement our system. To improve classification accuracy, two revised methods, self-learning and error filtering, for straight forward SVM results are proposed. The method of self-learning uses misclassified documents to retrain classification system, and the method of error filtering filters out possibly misclassified documents by analyzing the decision values from SVM. The experiment result on real-world data set shows the accuracy of basic SVM classification system is about 79% and the accuracy of improved SVM classification system can reach 83%.