Inproceedings,

A Comparative Study of Semi-naive Bayes Methods in Classification Learning

, and .
Proceedings of the Fourth Australasian Data Mining Conference (AusDM05), page 141-156. Sydney, University of Technology, (2005)

Abstract

Numerous techniques have sought to improve the accuracy of Naive Bayes (NB) by alleviating the attribute interdependence problem. This paper summarizes these semi-naive Bayesian methods into two groups: those that apply conventional NB with a new attribute set, and those that alter NB by allowing inter-dependencies between attributes. We review eight typical semi-naive Bayesian learning algorithms and perform error analysis using the bias-variance decomposition on thirty-six natural domains from the UCI Machine Learning Repository. In analysing the results of these experiments we provide general recommendations for selection between methods.

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