Extensions of the DECO3R algorithm for generating compact and cooperating Fuzzy Rule-based Classification Systems
Nikolaos Tsakiridis, John B. Theocharis, Raúl Pérez, Antonio González, George C. Zalidis · 2016
In this paper we propose two extensions of our previously introduced method DECO3R, a Fuzzy Rule-based Classification System (FRBCS). DECO3R stands for Differential Evolution based Cooperative and Competing learning of Compact FRBCS. It follows the Genetic Cooperative - Competitive Learning (GCCL) approach, and utilizes the Differential Evolution (DE) as its learning algorithm. Two novel schemes will be introduced, one which creates a number of parallel populations employing different DE strategies, and one inserting newly constructed features (relations and functions) in the antecedent part of the rules. These two new schemes, as well as the combination thereof, boost the performance of DECO3R, as evidenced by the results obtained, which were validated using non-parametric statistical tests.