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

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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.

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Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, 18071, Granada, Spain

    Rafael Alcalá, Jesús Alcalá-Fdez, Jorge Casillas, Oscar Cordón & Francisco Herrera

Authors
  1. Rafael Alcalá
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  2. Jesús Alcalá-Fdez
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  3. Jorge Casillas
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  4. Oscar Cordón
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  5. Francisco Herrera
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Corresponding author

Correspondence to Rafael Alcalá.

Additional information

This work has been supported by the spanish cicyt project tic2002-04036-c05-01 (keel).

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Alcalá, R., Alcalá-Fdez, J., Casillas, J. et al. Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling. Soft Comput 10, 717–734 (2006). https://doi.org/10.1007/s00500-005-0002-1

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