Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling

  • Authors:

  • Rafael Alcalá;Jesús Alcalá-Fdez;Jorge Casillas;Oscar Cordón;Francisco Herrera

  • Affiliations:

  • Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications
  • Year:
  • 2006

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Abstract

One of the problems associated to linguistic fuzzy modeling is its lack of accuracy when modeling some complex systems. To overcome this problem, many different possibilities of improving the accuracy of linguistic fuzzy modeling have been considered in the specialized literature. We will call these approaches as basic refinement approaches. In this work, we present a short study of how these basic approaches can be combined to obtain new hybrid approaches presenting a better trade-off between interpretability and accuracy. As an example of application of these kinds of systems, we analyze seven hybrid approaches to develop accurate and still interpretable fuzzy rule-based systems, which will be tested considering two real-world problems.