Survey of Improving Naive Bayes for Classification

Authors:
Liangxiao Jiang;Dianhong Wang;Zhihua Cai;Xuesong Yan
Affiliations:
Faculty of Computer Science, China University of Geosciences, Wuhan, Hubei, 430074, P.R. China;Faculty of Electronic Engineering, China University of Geosciences, Wuhan, Hubei, 430074, P.R. China;Faculty of Computer Science, China University of Geosciences, Wuhan, Hubei, 430074, P.R. China;Faculty of Computer Science, China University of Geosciences, Wuhan, Hubei, 430074, P.R. China
Venue:
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Year:
2007

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Abstract

The attribute conditional independence assumption of naive Bayes essentially ignores attribute dependencies and is often violated. On the other hand, although a Bayesian network can represent arbitrary attribute dependencies, learning an optimal Bayesian network classifier from data is intractable. Thus, learning improved naive Bayes has attracted much attention from researchers and presented many effective and efficient improved algorithms. In this paper, we review some of these improved algorithms and single out four main improved approaches: 1) Feature selection; 2) Structure extension; 3) Local learning; 4) Data expansion. We experimentally tested these approaches using the whole 36 UCI data sets selected by Weka, and compared them to naive Bayes. The experimental results show that all these approaches are effective. In the end, we discuss some main directions for future research on Bayesian network classifiers.