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.

中文翻译：

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使用改组蛙跳算法进行特征选择和模糊分类器设计

摘要

本文考虑了一种设计基于模糊规则的分类器的新方法。为了优化分类器的参数，应用了连续改组的蛙跳算法。在一组构造的分类器上，使用统计Akaike信息准则根据准确性和使用的特征数量选择最佳分类器。在15个KEEL数据集上测试了该方法的效率。将结果与类似算法的结果进行统计比较。本文提出的设计模糊分类器的新方法可以减少规则和属性的数量，从而提高分类结果的可解释性。