TITLE

A Bayesian Classifier Learning Algorithm Based on Optimization Model

AUTHOR(S)
Sanyang Liu; Mingmin Zhu; Youlong Yang
PUB. DATE
January 2013
SOURCE
Mathematical Problems in Engineering;2013, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
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-OMoffers a new opinion for the research of extended Bayesian classifier. Theoretical and experimental results show that the new algorithm is correct and effective.
ACCESSION #
94814303

 

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