DECO3RUM: A Differential Evolution learning approach for generating compact Mamdani fuzzy rule-based models

Abstract

Abstract In this paper, we propose a novel Evolutionary Mamdani Fuzzy Rule-based System named DECO3RUM (Differential Evolution based Cooperative and Competing learning of Compact Rule-based Models). The main advantage of DECO3RUM is that it offers simple and descriptive Mamdani models, without sacrificing the accuracy. It has further been designed to work with Big Data problems, where the number of patterns or features is significant. This is achieved through the successive use of three stages: (a) an instance selection stage, (b) an evolutionary rule base learning stage following the Genetic Cooperative Competitive Learning approach (where the novel Fuzzy Token Competition method based on AdaBoost tackles the cooperation-competition problem), which utilizes Differential Evolution as its learning algorithm, and (c) a post-processing stage to simplify the derived model and enhance its accuracy. DECO3RUM was validated using 24 datasets, and was additionally applied to one real-world high dimensional dataset from the domain of soil science. Through the comparisons with other state of the art evolutionary fuzzy systems it was demonstrated that DECO3RUM achieves a fair balance between accuracy and interpretability, performing better than its Mamdani counterparts.