Using Shuffled Frog-Leaping Algorithm for Feature Selection and Fuzzy Classifier Design

Abstract

This paper considers a new approach for designing fuzzy rule-based classifiers. To optimize the parameters of classifiers, a continuous shuffled frog-leaping algorithm is applied. On a set of constructed classifiers, the optimal classifier is selected in terms of the accuracy and the number of features used, using the statistical Akaike informational criterion. The efficiency of the proposed approach is tested on 15 KEEL data sets. The results are statistically compared with the results of similar algorithms. The new approach to designing fuzzy classifiers proposed in this article makes it possible to reduce the number of rules and attributes, thereby increasing the interpretability of classification results.