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

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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.

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    Authors and Affiliations

    1. Department of Computer Science, Higher Education Complex of Bam, Bam, Iran

      Fatemeh Barani & Mina Mirhosseini

    2. Department of Electrical Engineering, Shahid Bahonar University of Kerman, P.O. Box 76169-133, Kerman, Iran

      Hossein Nezamabadi-pour

    Authors
    1. Fatemeh Barani
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    2. Mina Mirhosseini
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    3. Hossein Nezamabadi-pour
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    Correspondence to Hossein Nezamabadi-pour.

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    All authors equally contributed and their names are in alphabetical order.

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    Barani, F., Mirhosseini, M. & Nezamabadi-pour, H. Application of binary quantum-inspired gravitational search algorithm in feature subset selection. Appl Intell 47, 304–318 (2017). https://doi.org/10.1007/s10489-017-0894-3

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