A hybrid method of sine cosine algorithm and differential evolution for feature selection

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie
PublisherSpringer Verlag
Pages145-155
Number of pages11
ISBN (Print)9783319701387
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10638 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Neural Information Processing, ICONIP 2017
CountryChina
CityGuangzhou
Period14.11.1718.11.17

Keywords

  • Differential evolution (DE)
  • Feature selection (FS)
  • Metaheuristic (MH)
  • Sine Cosine Algorithm (SCA)

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    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 D. Zhao, Y. Li, E-S. M. El-Alfy, D. Liu, & S. Xie (Eds.), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings (pp. 145-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10638 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-70139-4_15