Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning

Abstract

The feature selection (FS) is an important step for data analysis. FS is used to reduce the dimension of data by selecting the relevant features; while removing the redundant, noisy and irrelevant features that lead to degradation of the performance. Several swarm techniques are used to solve the FS problem and these methods provide results better than classical approaches. However, most of these techniques have limitations such as slow convergence and time complexity. These limitations occur due that all the agents update their position according to the best one. However, this best agent may be not the optimal global solution for FS, therefore, the swarm getting stuck in a local solution. This paper proposes an improved Runner-Root Algorithm (RRA). The RRA is combined with chaotic Singer map and opposition-based learning to increase its accuracy. The experiments are performed in eight datasets and the performance of the proposed method is compared against swarm algorithms.

Original language	English
Title of host publication	Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
Editors	Dongbin Zhao, Yuanqing Li, El-Sayed M. El-Alfy, Derong Liu, Shengli Xie
Publisher	Springer Verlag
Pages	156-166
Number of pages	11
ISBN (Print)	9783319701387
DOIs	https://doi.org/10.1007/978-3-319-70139-4_16
Publication status	Published - 2017
Externally published	Yes
Event	24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China Duration: 14 Nov 2017 → 18 Nov 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10638 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	24th International Conference on Neural Information Processing, ICONIP 2017
Country	China
City	Guangzhou
Period	14.11.17 → 18.11.17

Keywords

Chaotic map
Feature selection (FS)
Metaheuristic algorithms (MH)
Opposition-based learning (OBL)
Runner-Root Algorithm (RRA)
Swarm intelligence (SI)

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-70139-4_16

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Cite this

Ibrahim, R. A., Oliva, D., Ewees, A. A., & Lu, S. (2017). Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. 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. 156-166). (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_16

Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. / Ibrahim, Rehab Ali; Oliva, Diego; Ewees, Ahmed A.; Lu, Songfeng.

Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. ed. / Dongbin Zhao; Yuanqing Li; El-Sayed M. El-Alfy; Derong Liu; Shengli Xie. Springer Verlag, 2017. p. 156-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10638 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ibrahim, RA, Oliva, D, Ewees, AA & Lu, S 2017, Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. in D Zhao, Y Li, E-SM El-Alfy, D Liu & S Xie (eds), Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10638 LNCS, Springer Verlag, pp. 156-166, 24th International Conference on Neural Information Processing, ICONIP 2017, Guangzhou, China, 14.11.17. https://doi.org/10.1007/978-3-319-70139-4_16

Ibrahim RA, Oliva D, Ewees AA, Lu S. Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. In Zhao D, Li Y, El-Alfy E-SM, Liu D, Xie S, editors, Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. Springer Verlag. 2017. p. 156-166. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-70139-4_16

Ibrahim, Rehab Ali ; Oliva, Diego ; Ewees, Ahmed A. ; Lu, Songfeng. / Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings. editor / Dongbin Zhao ; Yuanqing Li ; El-Sayed M. El-Alfy ; Derong Liu ; Shengli Xie. Springer Verlag, 2017. pp. 156-166 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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