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

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Book cover Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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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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Acknowledgments

This work was in part supported by national key Research & Development Program of China (No. 2016YFD0101903), Nature Science Foundation of Hubei Province (Grant No. 2015CFA059), Science & Technology Pillar Program of Hubei Province (Grant No. 2014BAA146), Science & Technology Cooperation Program of Henan Province (No. 152106000048) and Hubei Collaborative Innovation Center of Basic Education Information technology Services.

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Mohamed E. Abd Elaziz, Pengfei Duan & Shengwu Xiong

  2. Department of Computer, Damietta University, Damietta, Egypt

    Ahmed A. Ewees

  3. Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI Av. Revolucion 1500, Guadalajara, Jalisco, Mexico

    Diego Oliva

  4. Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, Egypt

    Mohamed E. Abd Elaziz

Authors
  1. Mohamed E. Abd Elaziz
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  2. Ahmed A. Ewees
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  3. Diego Oliva
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  4. Pengfei Duan
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  5. Shengwu Xiong
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Corresponding author

Correspondence to Shengwu Xiong .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Abd Elaziz, M.E., Ewees, A.A., Oliva, D., Duan, P., Xiong, S. (2017). A Hybrid Method of Sine Cosine Algorithm and Differential Evolution for Feature Selection. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_15

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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