Improving Naïve Bayes Text Classifiers with Incremental Feature Weighting

A new term-weighting scheme for naïve Bayes text categorization

Purpose – Automatic text categorization has applications in several domains, for example e-mail spam detection, sexual content filtering, directory maintenance, and focused crawling, among others. Most information retrieval systems contain several components which use text categorization methods. One of the first text categorization methods was designed using a naı̈ve Bayes representation of the...

Poisson naive Bayes for text classification with feature weighting

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...

Combining Active Learning and Boosting for Naïve Bayes Text Classifiers

This paper presents a variant of the AdaBoost algorithm for boosting Näıve Bayes text classifier, called AdaBUS, which combines active learning with boosting algorithm. Boosting has been evaluated to effectively improve the accuracy of machine-learning based classifiers. However, Näıve Bayes classifier, which is remarkably successful in practice for text classification problems, is known not to...

Incremental Augmented Naive Bayes Classifiers

We propose two general heuristics to transform a batch Hill-climbing search into an incremental one. Our heuristics, when new data are available, study the search path to determine whether it is worth revising the current structure and if it is, they state which part of the structure must be revised. Then, we apply our heuristics to two Bayesian network structure learning algorithms in order to...

Effective Methods for Improving Naive Bayes Text Classifiers