Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data |
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Authors: | J Zhang D-K Kang A Silvescu V Honavar |
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Affiliation: | (1) Department of Computer Science, Artificial Intelligence Research Laboratory, Computational Intelligence, Learning, and Discovery Program, Iowa State University, Ames, Iowa 50011-1040, USA;(2) Department of Computer Science, Artificial Intelligence Research Laboratory; Computational Intelligence, Learning, and Discovery Program; Bioinformatics and Computational Biology Program, Iowa State University, Ames, Iowa 50011-1040, USA |
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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. |
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Keywords: | Attribute value taxonomies AVT-based na?ve Bayes learner Partially specified data |
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