Using shuffled frog-leaping algorithm for feature selection and fuzzy classifier design

Статья в журнале

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.

Журнал:

Scientific and Technical Information Processing
Allerton Press, Inc. (New York City)
Индексируется в Scopus, Web of Science

Библиографическая запись: Hodashinsky, I. A. Using Shuffled Frog-Leaping Algorithm for Feature Selection and Fuzzy Classifier Design [Electronic resource] / I. A. Hodashinsky, M. B. Bardamova, V. S. Kovalev // Scientific and Technical Information Processing. – 2019. – Vol. 46. – Iss. 6. – P. 381-387. – DOI: 10.3103/S0147688219060030

Ключевые слова:

FEATURE SELECTION FUZZY CLASSIFIER PARAMETERS OPTIMIZATION SHUFFLED FROG-LEAPING ALGORITHM

Индексируется в:

Авторы: Ходашинский И. А., Бардамова М. Б., Ковалёв В. С.

Год издания: 2019

Страницы: 381 - 387

Язык: Английский

DOI: 10.3103/S0147688219060030