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Candidate Elimination Criteria for Lazy Bayesian Rules

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AI 2001: Advances in Artificial Intelligence (AI 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2256))

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Abstract

Lazy Bayesian Rules modifies naive Bayesian classification to undo elements of the harmful attribute independence assumption. It has been shown to provide classification error comparable to boosting decision trees. This paper explores alternatives to the candidate elimination criterion employed within Lazy Bayesian Rules. Improvements over naive Bayes are consistent so long as the candidate elimination criteria ensures there is sufficient data for accurate probability estimation. However, the original candidate elimination criterion is demonstrated to provide better overall error reduction than the use of a minimum data subset size criterion.

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Author information

Authors and Affiliations

  1. School of Computing and Mathematics, Deakin University, 3217, Geelong, Vic.

    Geoffrey I. Webb

Authors
  1. Geoffrey I. Webb
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Editor information

Editors and Affiliations

  1. School of Computer and Information Science, University of South Australia, Mawson Lakes, 5095, SA, Australia

    Markus Stumptner  & Dan Corbett  & 

  2. Department of Computer Science, University of Adelaide, 5001, Adelaide, SA, Australia

    Mike Brooks

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© 2001 Springer-Verlag Berlin Heidelberg

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Webb, G.I. (2001). Candidate Elimination Criteria for Lazy Bayesian Rules. In: Stumptner, M., Corbett, D., Brooks, M. (eds) AI 2001: Advances in Artificial Intelligence. AI 2001. Lecture Notes in Computer Science(), vol 2256. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45656-2_47

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  • DOI: https://doi.org/10.1007/3-540-45656-2_47

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42960-9

  • Online ISBN: 978-3-540-45656-8

  • eBook Packages: Springer Book Archive

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