Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

Abstract Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, ‘It is not the strongest of the spec...

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Autores principales:	Jinyan Li, Simon Fong, Raymond K. Wong, Richard Millham, Kelvin K. L. Wong
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b1be323aba084e8ea579cefa3cc848a8
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spelling	oai:doaj.org-article:b1be323aba084e8ea579cefa3cc848a82021-12-02T15:05:09ZElitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets10.1038/s41598-017-04037-52045-2322https://doaj.org/article/b1be323aba084e8ea579cefa3cc848a82017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-04037-5https://doaj.org/toc/2045-2322Abstract Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, ‘It is not the strongest of the species that survives, but the most adaptable’. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.Jinyan LiSimon FongRaymond K. WongRichard MillhamKelvin K. L. WongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-14 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jinyan Li Simon Fong Raymond K. Wong Richard Millham Kelvin K. L. Wong Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets
description	Abstract Due to the high-dimensional characteristics of dataset, we propose a new method based on the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed approach uses the natural strategy established by Charles Darwin; that is, ‘It is not the strongest of the species that survives, but the most adaptable’. This means that in the evolution of a swarm, the elitists are motivated to quickly obtain more and better resources. The memory function helps the proposed method to avoid repeat searches for the worst position in order to enhance the effectiveness of the search, while the binary strategy simplifies the feature selection problem into a similar problem of function optimisation. Furthermore, the wrapper strategy gathers these strengthened wolves with the classifier of extreme learning machine to find a sub-dataset with a reasonable number of features that offers the maximum correctness of global classification models. The experimental results from the six public high-dimensional bioinformatics datasets tested demonstrate that the proposed method can best some of the conventional feature selection methods up to 29% in classification accuracy, and outperform previous WSAs by up to 99.81% in computational time.
format	article
author	Jinyan Li Simon Fong Raymond K. Wong Richard Millham Kelvin K. L. Wong
author_facet	Jinyan Li Simon Fong Raymond K. Wong Richard Millham Kelvin K. L. Wong
author_sort	Jinyan Li
title	Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets
title_short	Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets
title_full	Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets
title_fullStr	Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets
title_full_unstemmed	Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets
title_sort	elitist binary wolf search algorithm for heuristic feature selection in high-dimensional bioinformatics datasets
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b1be323aba084e8ea579cefa3cc848a8
work_keys_str_mv	AT jinyanli elitistbinarywolfsearchalgorithmforheuristicfeatureselectioninhighdimensionalbioinformaticsdatasets AT simonfong elitistbinarywolfsearchalgorithmforheuristicfeatureselectioninhighdimensionalbioinformaticsdatasets AT raymondkwong elitistbinarywolfsearchalgorithmforheuristicfeatureselectioninhighdimensionalbioinformaticsdatasets AT richardmillham elitistbinarywolfsearchalgorithmforheuristicfeatureselectioninhighdimensionalbioinformaticsdatasets AT kelvinklwong elitistbinarywolfsearchalgorithmforheuristicfeatureselectioninhighdimensionalbioinformaticsdatasets
_version_	1718388915697614848

Elitist Binary Wolf Search Algorithm for Heuristic Feature Selection in High-Dimensional Bioinformatics Datasets

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