On the influence of feature selection in fuzzy rule-based regression model generation

We have performed two experiments on twenty regression datasets. In the first experiment, we aimed to show the effectiveness of feature selection in fuzzy rule-based regression model generation by comparing the mean square errors achieved by the fuzzy rule-based models generated using all the features, and the features selected by FMIFS, NMIFS and CFS. In order to avoid possible biases related to the specific algorithm, we adopted the well-known Wang and Mendel algorithm for generating the fuzzy rule-based models. We present that the mean square errors obtained by models generated by using the features selected by FMIFS are on average similar to the values achieved by using all the features and lower than the ones obtained by employing the subset of features selected by NMIFS and CFS. In the second experiment, we intended to evaluate how feature selection can reduce the convergence time of the evolutionary fuzzy systems, which are probably the most effective fuzzy techniques for tackling regression problems. By using a state-of-the-art multi-objective evolutionary fuzzy system based on rule learning and membership function tuning, we show that the number of evaluations can be considerably reduced when pre-processing the dataset by feature selection.