Metaheuristic tuning of type-II fuzzy inference systems for data mining

Introduction of the fuzzy-set enabled the modeling of uncertain and noisy information. Type-2 fuzzy set took this further ahead by allowing fuzzy membership function to be fuzzy itself. In this work, we discussed an interval type-2 fuzzy inference system (IT2FIS). The training of the IT2FIS was provided in supervised manner by using metaheuristic algorithms. We comprehensively illustrated the formulation of the IT2FIS into an optimization problem. A precise genotype (a real vector) mapping of IT2FIS and a population-based strategy for optimum rule-base selection is described in this work. Since the IT2FIS learning is computationally difficult and costly, which we described in detail in this work, a comprehensive comparison between the performances of the metaheuristic algorithms were examined. The obtained results suggest that the IT2FIS learning was faster at the initial iterations of the metaheuristic learning, but tend to slow and get stuck in local minima. However, the metaheuristic algorithms, differential evaluation and bacteria foraging optimization offered significantly better results when compared to artificial bee colony, gray wolf optimization, particle swarm optimization and the other fuzzy inference models chosen for comparisons from literature.