GP-COACH: Genetic Programming-based learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. In this paper we propose GP-COACH, a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems. GP-COACH learns disjunctive normal form rules (generated by means of a context-free grammar) coded as one rule per tree. The population constitutes the rule base, so it is a genetic cooperative-competitive learning approach. GP-COACH uses a token competition mechanism to maintain the diversity of the population and this obliges the rules to compete and cooperate among themselves and allows the obtaining of a compact set of fuzzy rules. The results obtained have been validated by the use of non-parametric statistical tests, showing a good performance in terms of accuracy and interpretability.

References in zbMATH (referenced in 10 articles )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Derhami, Shahab; Smith, Alice E.: An integer programming approach for fuzzy rule-based classification systems (2017)
  2. Cano, Alberto; Zafra, Amelia; Ventura, Sebastián: An interpretable classification rule mining algorithm (2013) ioport
  3. Gálvez, Akemi; Iglesias, Andrés; Puig-Pey, Jaime: Iterative two-step genetic-algorithm-based method for efficient polynomial B-spline surface reconstruction (2012) ioport
  4. Muni, Durga Prasad; Pal, Nikhil R.: Evolution of fuzzy classifiers using genetic programming (2012) ioport
  5. Biglarbegian, Mohammad; Melek, William; Mendel, Jerry: On the robustness of type-1 and interval type-2 fuzzy logic systems in modeling (2011)
  6. Chan, Kit Yan; Dillon, Tharam S.; Kwong, C. K.: Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm (2011) ioport
  7. dos Santos, J. A.; Ferreira, C. D.; da S. Torres, R.; Gonçalves, M. A.; Lamparelli, R. A. C.: A relevance feedback method based on genetic programming for classification of remote sensing images (2011) ioport
  8. Martínez-Ballesteros, M.; Martínez-Álvarez, F.; Troncoso, A.; Riquelme, J. C.: An evolutionary algorithm to discover quantitative association rules in multidimensional time series (2011) ioport
  9. Yang, Dongdong; Jiao, Licheng; Gong, Maoguo; Liu, Fang: Artificial immune multi-objective SAR image segmentation with fused complementary features (2011) ioport
  10. Berlanga, F. J.; Rivera, A. J.; del Jesus, M. J.; Herrera, F.: GP-COACH: genetic programming-based learning of compact and accurate fuzzy rule-based classification systems for high-dimensional problems (2010) ioport