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Whale Optimisation Algorithm for high-dimensional small-instance feature selection.
- Source :
-
International Journal of Parallel, Emergent & Distributed Systems . Mar2021, Vol. 36 Issue 2, p80-96. 17p. - Publication Year :
- 2021
-
Abstract
- In this paper, eight variants of the Whale Optimisation Algorithm (WOA), that are based on eight different transfer functions, are introduced and used as search strategies in a wrapper feature selection model. Feature selection is a challenging task in machine learning process. It aims to minimise the size of a dataset by removing redundant and/or irrelevant features, with no information loss, to improve the efficiency of the learning algorithms. The used transfer functions belong to two different families; S-shaped and V-shaped. The proposed approaches have been tested on nine different high-dimensional medical datasets, with a low number of samples and multiple classes. The results revealed a superior performance for the V-shaped based approaches over the the S-shaped approaches. Moreover, the results of the V-shaped approach is compared with well-known feature selection approaches, and the superiority of the proposed approach is proven. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FEATURE selection
*MACHINE learning
*WHALES
*TRANSFER functions
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 17445760
- Volume :
- 36
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- International Journal of Parallel, Emergent & Distributed Systems
- Publication Type :
- Academic Journal
- Accession number :
- 148980676
- Full Text :
- https://doi.org/10.1080/17445760.2019.1617866