AVT-NBL: An algorithm for learning compact and accurate naïve bayes classifiers from attribute value taxonomies and data

Jim Zhang, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
PublisherIEEE Computer Society
Pages289-296
Number of pages8
ISBN (Print)0769521428, 9780769521428
DOIs
StatePublished - 2004
EventProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom
Duration: Nov 1 2004Nov 4 2004

Publication series

NameProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Other

OtherProceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Country/TerritoryUnited Kingdom
CityBrighton
Period11/1/0411/4/04

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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