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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2016

01.08.2016 | Original Article

A bumble bees mating optimization algorithm for the feature selection problem

verfasst von: Magdalene Marinaki, Yannis Marinakis

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2016

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Abstract

The feature selection problem is an interesting and important topic which is relevant for a variety of database applications. This paper utilizes a relatively new bees inspired optimization algorithm, the bumble bees mating optimization algorithm, to implement a feature subset selection procedure while the nearest neighbor classification method is used for the classification task. Several metrics are used in the nearest neighbor classification method, such as the euclidean distance, the standardized euclidean distance, the mahalanobis distance, the city block metric, the cosine distance and the correlation distance, in order to identify the most significant metric for the nearest neighbor classifier. The performance of the proposed algorithm is tested using various benchmark data sets from the UCI machine learning repository. The algorithm is compared with two other bees inspired algorithms, the one is based on the foraging behavior of the bees, the discrete artificial bee colony, and the other is based on the mating behavior of the bees, the honey bees mating optimization algorithm. The algorithm is, also, compared with a particle swarm optimization algorithm, an ant colony optimization algorithm, a genetic algorithm and with a number of algorithms from the literature.
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Metadaten
Titel
A bumble bees mating optimization algorithm for the feature selection problem
verfasst von
Magdalene Marinaki
Yannis Marinakis
Publikationsdatum
01.08.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2016
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0276-7

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