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A New Heuristic Function for DC*

  • Marco Lucarelli
  • Corrado Mencar
  • Ciro Castiello
  • Anna Maria Fanelli
Conference paper
  • 1.3k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8256)

Abstract

DC* (Double Clustering with A*) is an algorithm capable of generating highly interpretable fuzzy information granules from pre-classified data. These information granules can be used as bulding-blocks for fuzzy rule-based classifiers that exhibit a good tradeoff between interpretability and accuracy. DC* relies on A* for the granulation process, whose efficiency is tightly related to the heuristic function used for estimating the costs of candidate solutions. In this paper we propose a new heuristic function that is capable of exploiting class information to overcome the heuristic function originally used in DC* in terms of efficiency. The experimental results show that the proposed heuristic function allows huge savings in terms of computational effort, thus making DC* a competitive choice for designing interpretable fuzzy rule-based classifiers.

Keywords

Goal State Heuristic Function Fuzzy Information Fuzzy Partition Granulation Process 
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 International Publishing Switzerland 2013

Authors and Affiliations

  • Marco Lucarelli
    • 1
  • Corrado Mencar
    • 1
  • Ciro Castiello
    • 1
  • Anna Maria Fanelli
    • 1
  1. 1.Department of InformaticsUniversity of Bari “A. Moro”BariItaly

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