Abstract
Feature selection is an important data-preprocessing step that often precedes the classification task. Because of large amount of features in real world applications, feature selection is considered as a hard optimization problem. For such problems, metaheuristics have been shown to be a very promising solving approach. In this work, we propose to use Bee Swarm Optimization (BSO) for feature selection. The proposed algorithm, BSO-FS, is based on the wrapper approach that uses BSO for the generation of feature subsets, and a classifier algorithm to evaluate the solutions. BSO-FS is tested on well-known datasets and its performances are compared with those of recently published methods. Obtained results show that for the majority of datasets, BSO-FS selects efficiently relevant features while improving the classification accuracy.
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Sadeg, S., Hamdad, L., Benatchba, K., Habbas, Z. (2015). BSO-FS: Bee Swarm Optimization for Feature Selection in Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_33
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DOI: https://doi.org/10.1007/978-3-319-19258-1_33
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