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Integrating Rules and Cases in Learning via Case Explanation and Paradigm Shift

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 1952)

Abstract

In this article we discuss in detail two techniques for rule and case integration. Case-based learning is used when the rule language is exhausted. Initially, all the examples are used to induce a set of rules with satisfactory quality. The examples that are not covered by these rules are then handled as cases. The case-based approach used also combines rules and cases internally. Instead of only storing the cases as provided, it has a learning phase where, for each case, it constructs and stores a set of explanations with support and confidence above given thresholds. These explanations have different levels of generality and the maximally specific one corresponds to the case itself. The same case may have different explanations representing different perspectives of the case. Therefore, to classify a new case, it looks for relevant stored explanations applicable to the new case. The different possible views of the case given by the explanations correspond to considering different sets of conditions/features to analyze the case. In other words, they lead to different ways to compute similarity between known cases/explanations and the new case to be classified (as opposed to the commonly used fixed metric).

Keywords

  • Background Knowledge
  • Inductive Logic
  • Inductive Logic Programming
  • Case Integration
  • Satisfactory Quality

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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de Alneu, A.L., Alípio, J. (2000). Integrating Rules and Cases in Learning via Case Explanation and Paradigm Shift. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_5

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  • DOI: https://doi.org/10.1007/3-540-44399-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41276-2

  • Online ISBN: 978-3-540-44399-5

  • eBook Packages: Springer Book Archive