GP-COACH

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 9 articles )

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  1. Cano, Alberto; Zafra, Amelia; Ventura, Sebastián: An interpretable classification rule mining algorithm (2013)
  2. Gálvez, Akemi; Iglesias, Andrés; Puig-Pey, Jaime: Iterative two-step genetic-algorithm-based method for efficient polynomial B-spline surface reconstruction (2012)
  3. Muni, Durga Prasad; Pal, Nikhil R.: Evolution of fuzzy classifiers using genetic programming (2012)
  4. Biglarbegian, Mohammad; Melek, William; Mendel, Jerry: On the robustness of type-1 and interval type-2 fuzzy logic systems in modeling (2011)
  5. Chan, Kit Yan; Dillon, Tharam S.; Kwong, C.K.: Polynomial modeling for time-varying systems based on a particle swarm optimization algorithm (2011)
  6. 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)
  7. Martínez-Ballesteros, M.; Martínez-Álvarez, F.; Troncoso, A.; Riquelme, J.C.: An evolutionary algorithm to discover quantitative association rules in multidimensional time series (2011)
  8. Yang, Dongdong; Jiao, Licheng; Gong, Maoguo; Liu, Fang: Artificial immune multi-objective SAR image segmentation with fused complementary features (2011)
  9. 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)