Improvement of decision accuracy using discretization of continuous attributes

Authors:
QingXiang Wu;David Bell;Martin McGinnity;Girijesh Prasad;Guilin Qi;Xi Huang
Affiliations:
School of Physics and OptoElectronic Technology, Fujian Normal University, Fujian, Fuzhou, China;School of Computer Science, Queen's University, Belfast, UK;School of Computing and Intelligent Systems, University of Ulster at Magee, Londonderry, N.Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee, Londonderry, N.Ireland, UK;School of Computer Science, Queen's University, Belfast, UK;School of Physics and OptoElectronic Technology, Fujian Normal University, Fujian, Fuzhou, China
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

The naïve Bayes classifier has been widely applied to decision-making or classification. Because the naïve Bayes classifier prefers to dealing with discrete values, an novel discretization approach is proposed to improve naïve Bayes classifier and enhance decision accuracy in this paper. Based on the statistical information of the naïve Bayes classifier, a distributional index is defined in the new discretization approach. The distributional index can be applied to find a good solution for discretization of continuous attributes so that the naïve Bayes classifier can reach high decision accuracy for instance information systems with continuous attributes. The experimental results on benchmark data sets show that the naïve Bayes classifier with the new discretizer can reach higher accuracy than the C5.0 tree.