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Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification

Department of Security, Tomsk State University of Control Systems and Radioelectronics, 40 Lenina Prospect, 634050 Tomsk, Russia
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Symmetry 2019, 11(11), 1423; https://doi.org/10.3390/sym11111423
Received: 14 October 2019 / Revised: 11 November 2019 / Accepted: 15 November 2019 / Published: 18 November 2019
(This article belongs to the Special Issue Information Technologies and Electronics)
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. View Full-Text
Keywords: optimization; swallow swarm optimization; feature selection; wrapper method; fuzzy rule-based classifier optimization; swallow swarm optimization; feature selection; wrapper method; fuzzy rule-based classifier
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Hodashinsky, I.; Sarin, K.; Shelupanov, A.; Slezkin, A. Feature Selection Based on Swallow Swarm Optimization for Fuzzy Classification. Symmetry 2019, 11, 1423.

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