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Case-Based Reasoning Using Gradual Rules Induced from Dominance-Based Rough Approximations

  • Salvatore Greco
  • Benedetto Matarazzo
  • Roman Słowiński
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5009)

Abstract

Case-based reasoning (CBR) regards the inference of some proper conclusions related to a new situation by the analysis of similar cases from a memory of previous cases. We propose to represent similarity by gradual decision rules induced from rough approximations of fuzzy sets. Indeed, we are adopting the Dominance-based Rough Set Approach (DRSA) that is particularly appropriate in this context for its ability of handling monotonicity relationship of the type “the more similar is object y to object x, the more credible is that y belongs to the same set as x”. At the level of marginal similarity concerning single features, we consider only ordinal properties of similarity, and for the aggregation of marginal similarities, we use a set of gradual decision rules based on the general monotonicity property of comprehensive similarity with respect to marginal similarities. We present formal properties of rough approximations used for CBR.

Keywords

Single Feature Decision Attribute Monotonic Relationship Granular Computing Ordinal Property 
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 2008

Authors and Affiliations

  • Salvatore Greco
    • 1
  • Benedetto Matarazzo
    • 1
  • Roman Słowiński
    • 2
  1. 1.Faculty of EconomicsUniversity of CataniaCataniaItaly
  2. 2.Institute of Computing SciencePoznań University of Technology, 60-965 Poznań, and Systems Research Institute, Polish Academy of SciencesWarsawPoland

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