An Optimal Text Categorization Algorithm Based on SVM

Abstract

Text categorization is the process of assigning documents to a set of previously fixed categories. In this paper we develop an optimal SVM algorithm for text classification via multiple optimal strategies, such as a novel importance weight definition, the feature selection using the likelihood ratio for binomial distribution, the optimal parameter settings, etc. Comparison between our method and other conventional text classification algorithms is conducted on Reuter and TREC corpora. The experimental results indicate that our proposed algorithm yields much better performance than other conventional algorithms