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Accuracy vs. Interpretability of Fuzzy Rule-Based Classifiers: An Evolutionary Approach

  • Marian B. Gorzałczany
  • Filip Rudziński
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)

Abstract

The paper presents a generalization of the Pittsburgh approach to learn fuzzy classification rules from data. The proposed approach allows us to obtain a fuzzy rule-based system with a predefined level of compromise between its accuracy and interpretability (transparency). The application of the proposed technique to design the fuzzy rule-based classifier for the well known benchmark data sets (Dermatology and Wine) available from the http://archive.ics.uci.edu/ml is presented. A comparative analysis with several alternative (fuzzy) rule-based classification techniques has also been carried out.

Keywords

Membership Function Fuzzy Rule Rule Base Input Attribute Possibility Distribution 
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

  • Marian B. Gorzałczany
    • 1
  • Filip Rudziński
    • 1
  1. 1.Department of Electrical and Computer EngineeringKielce University of TechnologyKielcePoland

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