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Memetic Computing
Erschienen in: Memetic Computing 1/2015

01.03.2015 | Regular Research Paper

A memetic algorithm with support vector machine for feature selection and classification

verfasst von: Messaouda Nekkaa, Dalila Boughaci

Erschienen in: Memetic Computing | Ausgabe 1/2015

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Abstract

The memetic algorithm (MA) is an evolutionary metaheuristic that can be viewed as a hybrid genetic algorithm combined with some kinds of local search. In this paper, we propose a memetic algorithm combined with a support vector machine (SVM) for feature selection and classification in Data mining. The proposed approach tries to find a subset of features that maximizes the classification accuracy rate of SVM. In addition, another hybrid algorithm of MA and SVM with optimized parameters is also developed. The two versions of our proposed method are evaluated on some datasets and compared with some well-known classifiers for data classification. The computational experiments show that the hybrid method MA + SVM with optimized parameters provides competitive results and finds high quality solutions.
Vorheriger Artikel A BBO based framework for natural terrain identification in remote sensing

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Metadaten
Titel
A memetic algorithm with support vector machine for feature selection and classification
verfasst von
Messaouda Nekkaa
Dalila Boughaci
Publikationsdatum
01.03.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Memetic Computing / Ausgabe 1/2015
Print ISSN: 1865-9284
Elektronische ISSN: 1865-9292
DOI
https://doi.org/10.1007/s12293-015-0153-2

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