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

Jun Zhang, Dae Ki Kang, Adrian Silvescu, Vasant Honavar

Research output: Contribution to journalArticle

39 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)
Pages (from-to)157-179
Number of pages23
JournalKnowledge and Information Systems
Volume9
Issue number2
DOIs
StatePublished - Feb 1 2006

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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