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Neural Processing Letters
Published in: Neural Processing Letters 1/2016

01-08-2016

Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection

Authors: Messaouda Nekkaa, Dalila Boughaci

Published in: Neural Processing Letters | Issue 1/2016

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Abstract

Feature selection is a challenging task that has been the subject of a large amount of research, especially in relation to classification tasks. It permits to eliminate the redundant attributes and enhance the classification accuracy by keeping only the relevant attributes. In this paper, we propose a hybrid search method based on both harmony search algorithm (HSA) and stochastic local search (SLS) for feature selection in data classification. A novel probabilistic selection strategy is used in HSA–SLS to select the appropriate solutions to undergo stochastic local refinement, keeping a good compromise between exploration and exploitation. In addition, the HSA–SLS is combined with a support vector machine (SVM) classifier with optimized parameters. The proposed HSA–SLS method tries to find a subset of features that maximizes the classification accuracy rate of SVM. Experimental results show good performance in favor of our proposed method.
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Metadata
Title
Hybrid Harmony Search Combined with Stochastic Local Search for Feature Selection
Authors
Messaouda Nekkaa
Dalila Boughaci
Publication date
01-08-2016
Publisher
Springer US
Published in
Neural Processing Letters / Issue 1/2016
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9450-5

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