DECO3R: A Differential Evolution-based algorithm for generating compact Fuzzy Rule-based Classification Systems

摘要

In this paper a novel Genetic Fuzzy Rule-based Classification System, named DECO3R (Differential Evolution based Cooperative and Competing learning of Compact FRBCS), is proposed. DECO3R follows the genetic cooperative - competitive learning (GCCL) approach and uses Differential Evolution as its learning algorithm. In this frame, every chromosome encodes a single fuzzy rule. The proposed AdaBoost-based Fuzzy Token Competition (FTC) method is employed to deal with the cooperation - competition problem, an integral part to all GCCL algorithms. DECO3R learns clear, precise and predictive rules where the fuzzy sets in the premise part are consecutive. The experimental component analysis demonstrates that DE as a learning algorithm outperforms a simple Genetic Algorithm. Additionally, the novel FTC method exceeds the performance of other similar techniques. The experimental comparative analysis highlights the robust performance of DECO3R compared to other rule learning algorithms, both in terms of accuracy and of structural complexity.