Title: An improved Naive Bayesian classifier with advanced discretisation method

Authors: JunHua Zhao, Zhao Yang Dong, Xue Li

Addresses: School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia QLD 4072, Australia. ' School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia QLD 4072, Australia. ' School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia QLD 4072, Australia

Abstract: The Naive Bayesian (NB) classifiers have been one of the most popular techniques as basis of many classification applications both theoretically and practically. Our studies show that classification efficiencies, are very much dependent on the discretisation techniques, used in the Bayesian classifier, and formulation of such discretisation techniques therefore, becomes a critical issue. In this paper, we propose a novel discretisation technique whereby continuous attributes are divided into sufficient intervals, intersections of different class conditional density curves can be obtained and therefore, we are able to compute more precise approximations of the actual probability density as compared to traditional approaches. The Dirichlet prior assumption and its important property called perfect aggregation are presented to build a sound theoretical foundation for our methodology. Discussions on appropriate attribute divisions and the construction of new intervals have also been welldocumented. The developed technique is tested on UCI benchmark data sets. Results obtained are compared with other state-of-the-art techniques to illustrate the effectiveness of our new approach.

Keywords: naive Bayesian classifiers; discretisation; data mining; Dirichlet prior assumption; perfect aggregation.

DOI: 10.1504/IJISTA.2007.014262

International Journal of Intelligent Systems Technologies and Applications, 2007 Vol.3 No.3/4, pp.241 - 256

Published online: 28 Jun 2007 *

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