Random ensemble of fuzzy rule-based models.

Fuzzy rule-based models, due to modular architecture, have attracted attention and resulted in some practical implications because of their nonlinear characteristics and substantial interpretability. Data-driven fuzzy modeling is one of the most prevailing approaches, and the performance of such fuzzy models has been directly affected by a fundamental bias–variance dilemma. The concept and ensuing topologies of the ensemble strategy (bagging and boosting) offer an efficient method for constructing models to address this dilemma and to achieve a sound tradeoff. In this study, we design an ensemble fuzzy rule-based model in the setting of random forest and boosting mechanisms. To demonstrate the feasibility of the proposed method, we focus on the regression type of models. First, we design a method for assembling fuzzy rule-based models to improve the prediction accuracy. Second, we quantify the performance of the ensemble mechanism. To illustrate the effectiveness and discuss the main features of the proposed method, a series of publicly available datasets are considered in the experimental studies. • We design bagging and boosting mechanisms of assembling fuzzy rule-based models. • We quantify and analyze the performance of the ensemble mechanism. • We thoroughly study the predominant parameters of resulting ensemble models. [ABSTRACT FROM AUTHOR]