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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 975953, 9 pages
Research Article

A Bayesian Classifier Learning Algorithm Based on Optimization Model

Department of Mathematics, Xidian University, Xi'an 710071, China

Received 6 September 2012; Accepted 10 December 2012

Academic Editor: Cesar Cruz-Hernandez

Copyright © 2013 Sanyang Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Naive Bayes classifier is a simple and effective classification method, but its attribute independence assumption makes it unable to express the dependence among attributes and affects its classification performance. In this paper, we summarize the existing improved algorithms and propose a Bayesian classifier learning algorithm based on optimization model (BC-OM). BC-OM uses the chi-squared statistic to estimate the dependence coefficients among attributes, with which it constructs the objective function as an overall measure of the dependence for a classifier structure. Therefore, a problem of searching for an optimal classifier can be turned into finding the maximum value of the objective function in feasible fields. In addition, we have proved the existence and uniqueness of the numerical solution. BC-OM offers a new opinion for the research of extended Bayesian classifier. Theoretical and experimental results show that the new algorithm is correct and effective.