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Machine learning of credible classifications

  • Howard J. Hamilton
  • Ning Shan
  • Wojciech Ziarko
Machine Learning
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1342)

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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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Howard J. Hamilton
    • 1
  • Ning Shan
    • 1
  • Wojciech Ziarko
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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