Poisson naive Bayes for text classification with feature weighting

Abstract

In this paper, we investigate the use of multivariate Poisson model and feature weighting to learn naive Bayes text classifier. Our new naive Bayes text classification model assumes that a document is generated by a multivariate Poisson model while the previous works consider a document as a vector of binary term features based on the presence or absence of each term. We also explore the use of feature weighting for the naive Bayes text classification rather than feature selection, which is a quite costly process when a small number of the new training documents are continuously provided. Experimental results on the two test collections indicate that our new model with the proposed parameter estimation and the feature weighting technique leads to substantial improvements compared to the unigram language model classifiers that are known to outperform the original pure naive Bayes text classifiers.