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Ripple-Down Rules with Censored Production Rules

  • Yang Sok Kim
  • Paul Compton
  • Byeong Ho Kang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7457)

Abstract

Ripple-Down Rules (RDR) has been successfully used to implement incremental knowledge acquisition systems. Its success largely depends on the organisation of rules, and less attention has been paid to its knowledge representation scheme. Most RDR used standard production rules and exception rules. With sequential processing, RDR acquires exception rules for a particular rule only after the rule wrongly classifies cases. We propose censored production rules (CPR), to be used for acquiring exceptions when a new rule is created using censor conditions. This approach is useful when we have a large number of validation cases at hand. We discuss inference and knowledge acquisition algorithms and related issues. The approach can be combined with machine learning techniques to acquire censor conditions.

Keywords

Knowledge Acquisition Production Rule Minimum Description Length Inference Process Admission Status 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yang Sok Kim
    • 1
  • Paul Compton
    • 1
  • Byeong Ho Kang
    • 2
  1. 1.University of New South WalesSydneyAustralia
  2. 2.University of TasmaniaAustralia

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