DECO3R: Differential evolution based COoperative-COmpeting learning of COmpact fuzzy Rulebased classification systems
Nikolaos Tsakiridis, John B. Theocharis, George C. Zalidis · 2015
In this paper we propose a novel Fuzzy Rulebased Classification System, named DECO3R, which follows the genetic cooperative-competitive learning (GCCL) approach and uses the Differential Evolution algorithm as its genetic algorithm. DECO3R uses a novel Fuzzy Token Competition method implemented by AdaBoost which forces the rules to compete and cooperate with each other. It is capable of both learning clear and concise DNF rules, where the fuzzy sets are consecutive, and obtaining compact sets of rules. The obtained results have been validated using non-parametric statistical tests that demonstrate DECO3R's robust performance, both in terms of accuracy and of interpretability.