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Automatic Synthesis of Fuzzy Inference Systems for Classification

  • Jorge Paredes
  • Ricardo TanscheitEmail author
  • Marley Vellasco
  • Adriano Koshiyama
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 610)

Abstract

This work introduces AutoFIS-Class, a methodology for automatic synthesis of Fuzzy Inference Systems for classification problems. It is a data-driven approach, which can be described in five steps: (i) mapping of each pattern to a membership degree to fuzzy sets; (ii) generation of a set of fuzzy rule premises, inspired on a search tree, and application of quality criteria to reduce the exponential growth; (iii) association of a given premise to a suitable consequent term; (iv) aggregation of fuzzy rules to a same class and (v) decision on which consequent class is most compatible with a given pattern. The performance of AutoFIS-Class has been compared to those of other four rule-based systems for 21 datasets. Results show that AutoFIS-Class is competitive with respect to those systems, most of them evolutionary ones.

Keywords

Fuzzy inference system Automatic synthesis Classification 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Jorge Paredes
    • 1
  • Ricardo Tanscheit
    • 1
    Email author
  • Marley Vellasco
    • 1
  • Adriano Koshiyama
    • 1
  1. 1.Department of Electrical EngineeringPontifical Catholic University of Rio de JaneiroRio de JaneiroBrazil

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