An Improvement to Naive Bayes for Text Classification

Abstract

Naïve Bayes classifiers which are widely used for text classification in machine learning are based on the conditional probability of features belonging to a class, which the features are selected by feature selection methods. In this paper, an auxiliary feature method is proposed. It determines features by an existing feature selection method, and selects an auxiliary feature which can reclassify the text space aimed at the chosen features. Then the corresponding conditional probability is adjusted in order to improve classification accuracy. Illustrative examples show that the proposed meth-od indeed improves the performance of naïve Bayes classifier.