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

Rehab Ali Ibrahim, Diego Oliva, Ahmed A. Ewees, Songfeng Lu

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

14 Citations (Scopus)

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 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
Pages156-166
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

  • 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)

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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