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2017 | OriginalPaper | Buchkapitel

A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection

verfasst von : Mohamed E. Abd Elaziz, Ahmed A. Ewees, Diego Oliva, Pengfei Duan, Shengwu Xiong

Erschienen in: Neural Information Processing

Verlag: Springer International Publishing

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Abstract

The feature selection is an important step to improve the performance of classifier through reducing the dimension of the dataset, so the time complexity and space complexity are reduced. There are several feature selection methods are used the swarm techniques to determine the suitable subset of features. The sine cosine algorithm (SCA) is one of the recent swarm techniques that used as global optimization method to solve the feature selection, however, it can be getting stuck in local optima. In order to solve this problem, the differential evolution operators are used as local search method which helps the SCA to skip the local point. The proposed method is compared with other three algorithms to select the subset of features used eight UCI datasets. The experiments results showed that the proposed method provided better results than other methods in terms of performance measures and statistical test.

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Titel: A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection
verfasst von: Mohamed E. Abd Elaziz
Ahmed A. Ewees
Diego Oliva
Pengfei Duan
Shengwu Xiong
Verlag: Springer International Publishing
Buch: Neural Information Processing
Print ISBN: 978-3-319-70138-7

Electronic ISBN: 978-3-319-70139-4

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-70139-4_15

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