An Empirical Study of Double-Bridge Search Move on Subset Feature Selection Search of Bees Algorithm

Authors

  • Aras Ghazi Mohammed Al-dawoodi School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia. College of Computer and Mathematical Science, Tikrit University, Tikrit, Iraq.
  • M. Mahmuddin School of Computing, College of Arts and Sciences, Universiti Utara Malaysia, 06010 Sintok, Kedah, Malaysia.

Keywords:

Wrapper Feature Selection, Local Neighbourhoods Search, Data Mining.

Abstract

The application of Bees Algorithm in wrapper feature selection (BAFS) has been implemented but yet too far from perfect and has few weaknesses. The algorithm performs combination of exploitative neighbourhoods and random explorative search. This creates a heavy computational time, and in the same time could affect the overall accuracy subset selection. To rectify this issue, a double-bridge move proposed and benchmark dataset have been used to determine the performance of the proposed method. The obtained results from the experiment confirmed that the proposed extension of the search neighbourhood have provided better accuracy with suitable time than the original BAFS.

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Published

2017-06-01

How to Cite

Mohammed Al-dawoodi, A. G., & Mahmuddin, M. (2017). An Empirical Study of Double-Bridge Search Move on Subset Feature Selection Search of Bees Algorithm. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(2-2), 11–15. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2212

Issue

Vol. 9 No. 2-2: Circuit and System Advancement in Modern World I

Section

Articles

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