Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets

A microarray is a collection of DNA sequences that reflect an organism's whole gene set and are organized in a grid pattern for use in genetic testing. Microarray datasets are extremely high-dimensional and have a very small sample size, posing the challenges of insufficient data and high compu...

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Detalles Bibliográficos
Autores: Theng, Dipti, Bhoyar, Kishor K
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/160189
Acceso en línea:http://hdl.handle.net/10366/160189
Access Level:acceso abierto
Palabra clave:feature selection
stability of feature selection
whale optimization algorithm
marine predator algorithm
grey wolf optimization
microarray datasets
high dimensional datasets
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spelling Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray DatasetsTheng, DiptiBhoyar, Kishor Kfeature selectionstability of feature selectionwhale optimization algorithmmarine predator algorithmgrey wolf optimizationmicroarray datasetshigh dimensional datasetsA microarray is a collection of DNA sequences that reflect an organism's whole gene set and are organized in a grid pattern for use in genetic testing. Microarray datasets are extremely high-dimensional and have a very small sample size, posing the challenges of insufficient data and high computational complexity. Identification of true biomarkers that are the most significant features (a very small subset of the complete feature set) is desired to solve these issues. This reduces over-fitting, and time complexity, and improves model generalization. Various feature selection algorithms are used for this biomarker identification. This research proposed a modification to the whale optimization algorithm (WOAm) for biomarker discovery, in which the fitness of each search agent is evaluated using the hinge loss function during the hunting for prey phase to determine the optimal search agent. Also compared the results of the proposed modified algorithm with the original whale optimization algorithm and also with contemporary algorithms like the marine predator algorithm and grey wolf optimization. All these algorithms are evaluated on six different high-dimensional microarray datasets. It has been observed that the proposed modification for the whale optimization algorithm has significantly improved the results of feature selection across all the datasets. Domain experts trust the resultant biomarker/ associated genes by the stability of the results obtained. The chosen feature set's stability was also evaluated during the research work. According to the findings, our proposed WOAm has superior stability compared to other algorithms for the CNS, colon, Leukemia, and OSCC. datasets.Ediciones Universidad de Salamanca (España)202420242023info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10366/160189reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1601892026-06-07T06:28:51Z
dc.title.none.fl_str_mv Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
title Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
spellingShingle Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
Theng, Dipti
feature selection
stability of feature selection
whale optimization algorithm
marine predator algorithm
grey wolf optimization
microarray datasets
high dimensional datasets
title_short Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
title_full Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
title_fullStr Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
title_full_unstemmed Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
title_sort Stable Feature Selection using Improved Whale Optimization Algorithm for Microarray Datasets
dc.creator.none.fl_str_mv Theng, Dipti
Bhoyar, Kishor K
author Theng, Dipti
author_facet Theng, Dipti
Bhoyar, Kishor K
author_role author
author2 Bhoyar, Kishor K
author2_role author
dc.subject.none.fl_str_mv feature selection
stability of feature selection
whale optimization algorithm
marine predator algorithm
grey wolf optimization
microarray datasets
high dimensional datasets
topic feature selection
stability of feature selection
whale optimization algorithm
marine predator algorithm
grey wolf optimization
microarray datasets
high dimensional datasets
description A microarray is a collection of DNA sequences that reflect an organism's whole gene set and are organized in a grid pattern for use in genetic testing. Microarray datasets are extremely high-dimensional and have a very small sample size, posing the challenges of insufficient data and high computational complexity. Identification of true biomarkers that are the most significant features (a very small subset of the complete feature set) is desired to solve these issues. This reduces over-fitting, and time complexity, and improves model generalization. Various feature selection algorithms are used for this biomarker identification. This research proposed a modification to the whale optimization algorithm (WOAm) for biomarker discovery, in which the fitness of each search agent is evaluated using the hinge loss function during the hunting for prey phase to determine the optimal search agent. Also compared the results of the proposed modified algorithm with the original whale optimization algorithm and also with contemporary algorithms like the marine predator algorithm and grey wolf optimization. All these algorithms are evaluated on six different high-dimensional microarray datasets. It has been observed that the proposed modification for the whale optimization algorithm has significantly improved the results of feature selection across all the datasets. Domain experts trust the resultant biomarker/ associated genes by the stability of the results obtained. The chosen feature set's stability was also evaluated during the research work. According to the findings, our proposed WOAm has superior stability compared to other algorithms for the CNS, colon, Leukemia, and OSCC. datasets.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/160189
url http://hdl.handle.net/10366/160189
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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