Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data

Authors:
J. Zhang;D.-K. Kang;A. Silvescu;V. Honavar
Affiliations:
Department of Computer Science, Artificial Intelligence Research Laboratory, Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, Iowa;Department of Computer Science, Artificial Intelligence Research Laboratory, Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, Iowa;Department of Computer Science, Artificial Intelligence Research Laboratory, Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, Iowa;Department of Computer Science, Artificial Intelligence Research Laboratory/ Computational Intelligence, Learning, and Discovery Program/ Bioinformatics and Computational Biology Program, Iowa Sta ...
Venue:
Knowledge and Information Systems
Year:
2006

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Abstract

In many application domains, there is a need for learning algorithms that can effectively exploit attribute value taxonomies (AVT)--hierarchical groupings of attribute values--to learn compact, comprehensible and accurate classifiers from data--including data that are partially specified. This paper describes AVT-NBL, a natural generalization of the naïve Bayes learner (NBL), for learning classifiers from AVT and data. Our experimental results show that AVT-NBL is able to generate classifiers that are substantially more compact and more accurate than those produced by NBL on a broad range of data sets with different percentages of partially specified values. We also show that AVT-NBL is more efficient in its use of training data: AVT-NBL produces classifiers that outperform those produced by NBL using substantially fewer training examples.