Classification of text documents

Abstract

We investigate four different classification methods for document classification: the naive Bayes classifier, nearest neighbor classifier, decision tree classifier, and subspace method. The classifiers were applied to seven-class Yahoo newsgroups individually and in combination. We study three classifier combination approaches: simple voting, dynamic classifier selection, and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classification methods on our data sets. Combinations of multiple classifiers did not always improve classification accuracy. Among the three different combination approaches, the adaptive classifier combination method proposed here performed the best.