Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

Статья в журнале

This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.

Журнал:

• Symmetry
• Symmetry (Basel)
• Индексируется в Web of Science

Библиографическая запись: Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification / I. Hodashinsky [et. al.] // Symmetry. - 2019. - Vol. 11. - pp. 1-16. - DOI: 10.3390/sym11111423