Genetic Fuzzy Systems (GFSs) are models capable of integrating accuracy and high comprehensibility in their results. In the case of GFSs for classification, more emphasis has been given to improving the “Genetic” component instead of its “Fuzzy” counterpart. This paper focus on the Fuzzy Inference component to obtain a more accurate and interpretable system, presenting the so-called Genetic Programming Fuzzy Inference System for Classification (GPFIS-CLASS). This model is based on Multi-Gene Genetic Programming and aims to explore the elements of a Fuzzy Inference System. GPFIS-CLASS has the following features: (i) it builds fuzzy rules premises employing t-norm, t-conorm, negation and linguistic hedge operators; (ii) it associates to each rule premise a suitable consequent term; and (iii) it improves the aggregation process by using a weighted mean computed by restricted least squares. It has been evaluated in two sets of benchmarks, comprising a total of 45 datasets, and has been compared with eight different classifiers, six of them based on GFSs. The results obtained in both sets demonstrate that GPFIS-CLASS provides better results for most benchmark datasets. 
GPFIS-CLASS: A Genetic Fuzzy System based on Genetic Programming for classification problems