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Evaluating Case Selection Algorithms for Analogical Reasoning Systems

  • Eduardo Lupiani
  • Jose M. Juarez
  • Fernando Jimenez
  • Jose Palma
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

An essential issue for developing analogical reasoning systems (such as Case-Based Reasoning systems) is to build the case memory by selecting registers from an external database. This issue is called case selection and the literature provides a wealth of algorithms to deal with it. For any particular domain, to choose the case selection algorithm is a critical decision on the system design. Despite some algorithms obtain good results, a specific algorithms evaluation is needed. Most of the efforts done in this line focus on the number of registers selected and providing a simple evaluation of the system obtained. In some domains, however, the system must fulfil certain constraints related to accuracy or efficiency. For instance, in the medical field, specificity and sensitivity are critical values for some tests. In order to partially solve this problem, we propose an evaluation methodology to obtain the best case selection method for a given memory case. In order to demonstrate the usefulness of this methodology, we present new case selection algorithms based on evolutionary multi-objective optimization. We compare the classical algorithms and the multi-objective approach in order to select the most suitable case selection algorithm according to different standard problems.

Keywords

Case Selection Evaluation Methodology Reasoning System Breast Cancer Dataset Neighbor Rule 
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 2011

Authors and Affiliations

  • Eduardo Lupiani
    • 1
  • Jose M. Juarez
    • 1
  • Fernando Jimenez
    • 1
  • Jose Palma
    • 1
  1. 1.Computer Science FacultyUniversidad de MurciaSpain

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