Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach

Results show that the proposed scheme for candidate rule selection leads to an improvement in the results, regardless of the subsequent search algorithm. Moreover, local search-based algorithms achieve competitive results, reducing substantially the number of rules of the learnt systems.