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Published Articles >> Table of Contents >> Abstract
Fourth IEEE International Conference on Data Mining (ICDM'04)
pp. 289-296
AVT-NBL: An Algorithm for Learning Compact and Accurate Naïve Bayes Classifiers from Attribute Value Taxonomies and Data
Jun Zhang, Iowa State University, Ames
Vasant Honavar, Iowa State University, Ames
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10083
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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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 Additional Information
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Citation:
Jun Zhang, Vasant Honavar,
"AVT-NBL: An Algorithm for Learning Compact and Accurate Naïve Bayes Classifiers from Attribute Value Taxonomies and Data,"
icdm,
pp. 289-296,
Fourth IEEE International Conference on Data Mining (ICDM'04),
2004
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