Binary Political Optimizer for Feature Selection Using Gene Expression Data
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-29
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment.
One of the most significant challenges in this research field is to select high relevant genes from gene expression data.
To address this problem, feature selection is a well-known technique to eliminate unnecessary genes in order to ensure accurate classification results.
This paper proposes a binary version of Political Optimizer (PO) to solve feature selection problem using gene expression data.
Two transfer functions are used to design a binary PO.
The first one is based on Sigmoid function and will be noted as BPO-S, while the second one is based on V-shaped function and will be noted as BPO-V.
The proposed methods are evaluated using 9 biological datasets and compared with 8 binary well-known metaheuristics.
The comparative results show the prevalent performance of the BPO methods especially BPO-V in comparison with other techniques.
American Psychological Association (APA)
Manita, Ghaith& Korbaa, Ouajdi. 2020. Binary Political Optimizer for Feature Selection Using Gene Expression Data. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1138966
Modern Language Association (MLA)
Manita, Ghaith& Korbaa, Ouajdi. Binary Political Optimizer for Feature Selection Using Gene Expression Data. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1138966
American Medical Association (AMA)
Manita, Ghaith& Korbaa, Ouajdi. Binary Political Optimizer for Feature Selection Using Gene Expression Data. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1138966
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138966