Generation of first-order TSK rules based on the apriori + search approach

In this work, we propose several algorithms for learning first-order TSK fuzzy rules. These methods, consist of two stages: first, they generate a set of candidate rules with an adaptation of the apriori algorithm for frequent itemset detection. Then, they select a subset of such rules, generally by means of a search algorithm. In this work we have tested a genetic and two different local search algorithms. The results obtained show that, genetic algorithms tends to converge to systems with a higher number of rules, which minimize the training error, but also overfit. On the other hand, local search gets stuck in configurations with fewer rules which, despite producing a higher training error, avoid overfitting and lead to best results in terms of error.