Application of binary quantum-inspired gravitational search algorithm in feature subset selection

Application of binary quantum-inspired gravitational search algorithm in feature subset selection Feature selection is an important task to improve prediction accuracy of classifiers and to decrease the problem size. Several approaches have been presented to perform feature selection using metaheuristic algorithms. In this paper, we employ the binary quantum-inspired gravitational search algorithm (BQIGSA) combined with the k-nearest neighbor classifier as a wrapper approach to select a (sub-) optimal subset of features. We evaluate the proposed approach on several well-known datasets and compare our approach with other similar state-of-the-art feature selection techniques. Comparative results verify the acceptable performance of the proposed approach in feature selection. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Application of binary quantum-inspired gravitational search algorithm in feature subset selection

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-017-0894-3
Publisher site
See Article on Publisher Site

Abstract

Feature selection is an important task to improve prediction accuracy of classifiers and to decrease the problem size. Several approaches have been presented to perform feature selection using metaheuristic algorithms. In this paper, we employ the binary quantum-inspired gravitational search algorithm (BQIGSA) combined with the k-nearest neighbor classifier as a wrapper approach to select a (sub-) optimal subset of features. We evaluate the proposed approach on several well-known datasets and compare our approach with other similar state-of-the-art feature selection techniques. Comparative results verify the acceptable performance of the proposed approach in feature selection.

Journal

Applied IntelligenceSpringer Journals

Published: Mar 10, 2017

References

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