@article{354,
  keywords = {classification, Fuzzy rule-based systems, Genetic Fuzzy Systems, Genetic programming, High-dimensional problems, Interpretability-accuracy trade-off},
  author = {Francisco Berlanga and Antonio Jesús Rivera Rivas and Maria José del Jesus Díaz and Francisco Herrera Triguero},
  title = {GP-COACH: Genetic Programming-based learning of Compact and ACcurate fuzzy rule-based classification systems for High-dimensional problems},
  abstract = {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.},
  year = {2010},
  journal = {Information Sciences},
  volume = {180},
  number = {8},
  pages = {1183-1200},
  issn = {0020-0255},
  url = {http://www.sciencedirect.com/science/article/pii/S0020025509005635},
  doi = {10.1016/j.ins.2009.12.020},
}