Random one-dependence estimators

Authors:
Liangxiao Jiang
Affiliations:
Department of Computer Science, China University of Geosciences, Wuhan, Hubei 430074, China
Venue:
Pattern Recognition Letters
Year:
2011

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Abstract

Many approaches attempt to improve naive Bayes and have been broadly divided into five main categories: (1) structure extension; (2) attribute weighting; (3) attribute selection; (4) instance weighting; (5) instance selection, also called local learning. In this paper, we work on the approach of structure extension and single out a random Bayes model by augmenting the structure of naive Bayes. We called it random one-dependence estimators, simply RODE. In RODE, each attribute has at most one parent from other attributes and this parent is randomly selected from log"2m (where m is the number of attributes) attributes with the maximal conditional mutual information. Our work conducts the randomness into Bayesian network classifiers. The experimental results on a large number of UCI data sets validate its effectiveness in terms of classification, class probability estimation, and ranking.