Hancer 2015 Binary ABC algorithm based on advanced similarity scheme.pdf (401.27 kB)

A binary ABC algorithm based on advanced similarity scheme for feature selection

Download (401.27 kB)
journal contribution
posted on 25.03.2021, 03:30 by E Hancer, Bing Xue, D Karaboga, Mengjie Zhang
© 2015 Elsevier B.V. All rights reserved. Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers' attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection.

History

Preferred citation

Hancer, E., Xue, B., Karaboga, D. & Zhang, M. (2015). A binary ABC algorithm based on advanced similarity scheme for feature selection. Applied Soft Computing Journal, 36, 334-348. https://doi.org/10.1016/j.asoc.2015.07.023

Journal title

Applied Soft Computing Journal

Volume

36

Publication date

23/08/2015

Pagination

334-348

Publisher

Elsevier BV

Publication status

Published

ISSN

1568-4946

eISSN

1872-9681

Language

en

Exports