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TSK-0 Fuzzy Rule-Based Systems for High-Dimensional Problems Using the Apriori Principle for Rule Generation

  • Javier Cózar
  • Luis de la Ossa
  • José A. Gámez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8536)

Abstract

Algorithms which learn Linguistic Fuzzy Rule-Based Systems from data usually start up from the definition of the linguistic variables, generate a set of candidate rules and, afterwards, search a subset of them through a metaheuristic technique. In high-dimensional datasets the number of candidate rules is intractable, and a preselection is a must. This work adapts an existing preselection algorithm for Fuzzy Asociation Rule-Based Classification Systems to deal with TSK-0 LFRBSs. Experimental results show a good behaviour of the adaptation allowing to build precise and simple models for high-dimensional problems.

Keywords

linguistic fuzzy modeling machine learning high- dimensional Takagi-Sugeno-Kang 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Javier Cózar
    • 1
  • Luis de la Ossa
    • 1
  • José A. Gámez
    • 1
  1. 1.University of Castilla-La ManchaAlbaceteSpain

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