M. J. del Jesus

TL;DR: KEEL as discussed by the authors is a software tool to assess evolutionary algorithms for data mining problems of various kinds including regression, classification, unsupervised learning, etc., which includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL.

TL;DR: A genetic tuning process for jointly fitting the fuzzy rule symbolic representations and the meaning of the involved membership functions and the use of linguistic hedges to perform slight modifications keeping a good interpretability is introduced.

TL;DR: A novel Adaboost algorithm to learn fuzzy-rule-based classifiers using evolutionary boosting scheme and performance comparisons between the proposed method and other classification schemes applied on a set of benchmark classification tasks are assessed.

TL;DR: This work presents a genetic feature selection process that can be integrated in a multistage genetic learning method to obtain, in a more efficient way, FRBCSs composed of a set of comprehensible fuzzy rules with high-classification ability.

TL;DR: GP-COACH is a Genetic Programming-based method for the learning of COmpact and ACcurate fuzzy rule-based classification systems for High-dimensional problems that uses a token competition mechanism to maintain the diversity of the population.