Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data.
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.
Zhang
J
J
Department of Computer Science, Artificial Intelligence Research Laboratory, Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, Iowa 50011-1040, USA jzhang@cs.iastate.edu.
Kang
D-K
DK
Silvescu
A
A
Honavar
V
V