Improving naive Bayes classifier using conditional probabilities

Authors:
Sona Taheri;Musa Mammadov;Adil M. Bagirov
Affiliations:
University of Ballarat, Victoria, Australia;University of Ballarat, Victoria, Australia and National Information and Communications Technology Australia (NICTA);University of Ballarat, Victoria, Australia
Venue:
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
Year:
2011

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Abstract

Naive Bayes classifier is the simplest among Bayesian Network classifiers. It has shown to be very efficient on a variety of data classification problems. However, the strong assumption that all features are conditionally independent given the class is often violated on many real world applications. Therefore, improvement of the Naive Bayes classifier by alleviating the feature independence assumption has attracted much attention. In this paper, we develop a new version of the Naive Bayes classifier without assuming independence of features. The proposed algorithm approximates the interactions between features by using conditional probabilities. We present results of numerical experiments on several real world data sets, where continuous features are discretized by applying two different methods. These results demonstrate that the proposed algorithm significantly improve the performance of the Naive Bayes classifier, yet at the same time maintains its robustness.