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
Ключевые слова:
OPTIMIZATION SWALLOW SWARM OPTIMIZATION FEATURE SELECTION WRAPPER METHOD FUZZY RULE-BASED CLASSIFIER