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research
Alleviating Naive Bayes attribute independence assumption by attribute weighting
Authors
Mark J. Carman
Jesús Cerquides
Geoffrey I. Webb
Nayyar A. Zaidi
Publication date
24 May 2016
Publisher
Doi
Abstract
Despite the simplicity of the Naive Bayes classifier, it has continued to perform well against more sophisticated newcomers and has remained, therefore, of great interest to the machine learning community. Of numerous approaches to refining the naive Bayes classifier, attribute weighting has received less attention than it warrants. Most approaches, perhaps influenced by attribute weighting in other machine learning algorithms, use weighting to place more emphasis on highly predictive attributes than those that are less predictive. In this paper, we argue that for naive Bayes attribute weighting should instead be used to alleviate the conditional independence assumption. Based on this premise, we propose a weighted naive Bayes algorithm, called WANBIA, that selects weights to minimize either the negative conditional log likelihood or the mean squared error objective functions. We perform extensive evaluations and find that WANBIA is a competitive alternative to state of the art classifiers like Random Forest, Logistic Regression and A1DE. © 2013 Nayyar A. Zaidi, Jesus Cerquides, Mark J. Carman and Geoffrey I. Webb.This research has been supported by the Australian Research Council under grant DP110101427 and Asian Office of Aerospace Research and Development, Air Force Office of Scientific Research under contract FA23861214030. The authors would like to thank Mark Hall for providing the code for CFS and MH. The authors would also like to thank anonymous reviewers for their insightful comments that helped improving the paper tremendously.Peer Reviewe
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Last time updated on 18/08/2016