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Machine learning of credible classifications

  • Machine Learning
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Advanced Topics in Artificial Intelligence (AI 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1342))

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Abstract

We present an approach to concept discovery in machine learning based on searching for maximally general credible classifications. To be credible, a classification must provide decisions for all or nearly all possible values of the condition attributes, and these decisions must be adequately supported by evidence. Our objective is to find a classification for a domain that meets predefined quality criteria. For example, a classification can be sought whose coverage of the domain exceeds a user-defined threshold and whose decisions are supported by sufficient input instances.

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References

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Authors and Affiliations

  1. Department of Computer Science, University of Regina, S4S 0A2, Regina, Saskatchewan, Canada

    Howard J. Hamilton, Ning Shan & Wojciech Ziarko

Authors
  1. Howard J. Hamilton
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  2. Ning Shan
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  3. Wojciech Ziarko
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Editor information

Abdul Sattar

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© 1997 Springer-Verlag Berlin Heidelberg

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Hamilton, H.J., Shan, N., Ziarko, W. (1997). Machine learning of credible classifications. In: Sattar, A. (eds) Advanced Topics in Artificial Intelligence. AI 1997. Lecture Notes in Computer Science, vol 1342. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63797-4_86

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  • DOI: https://doi.org/10.1007/3-540-63797-4_86

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63797-4

  • Online ISBN: 978-3-540-69649-0

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