A novel feature selection algorithm based on damping oscillation theory.

Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is p...

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Autores principales:	Fujun Wang, Xing Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4d36281829074bf3844ad192f3430b27
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id	oai:doaj.org-article:4d36281829074bf3844ad192f3430b27
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spelling	oai:doaj.org-article:4d36281829074bf3844ad192f3430b272021-12-02T20:18:35ZA novel feature selection algorithm based on damping oscillation theory.1932-620310.1371/journal.pone.0255307https://doaj.org/article/4d36281829074bf3844ad192f3430b272021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255307https://doaj.org/toc/1932-6203Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.Fujun WangXing WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255307 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fujun Wang Xing Wang A novel feature selection algorithm based on damping oscillation theory.
description	Feature selection is an important task in big data analysis and information retrieval processing. It reduces the number of features by removing noise, extraneous data. In this paper, one feature subset selection algorithm based on damping oscillation theory and support vector machine classifier is proposed. This algorithm is called the Maximum Kendall coefficient Maximum Euclidean Distance Improved Gray Wolf Optimization algorithm (MKMDIGWO). In MKMDIGWO, first, a filter model based on Kendall coefficient and Euclidean distance is proposed, which is used to measure the correlation and redundancy of the candidate feature subset. Second, the wrapper model is an improved grey wolf optimization algorithm, in which its position update formula has been improved in order to achieve optimal results. Third, the filter model and the wrapper model are dynamically adjusted by the damping oscillation theory to achieve the effect of finding an optimal feature subset. Therefore, MKMDIGWO achieves both the efficiency of the filter model and the high precision of the wrapper model. Experimental results on five UCI public data sets and two microarray data sets have demonstrated the higher classification accuracy of the MKMDIGWO algorithm than that of other four state-of-the-art algorithms. The maximum ACC value of the MKMDIGWO algorithm is at least 0.5% higher than other algorithms on 10 data sets.
format	article
author	Fujun Wang Xing Wang
author_facet	Fujun Wang Xing Wang
author_sort	Fujun Wang
title	A novel feature selection algorithm based on damping oscillation theory.
title_short	A novel feature selection algorithm based on damping oscillation theory.
title_full	A novel feature selection algorithm based on damping oscillation theory.
title_fullStr	A novel feature selection algorithm based on damping oscillation theory.
title_full_unstemmed	A novel feature selection algorithm based on damping oscillation theory.
title_sort	novel feature selection algorithm based on damping oscillation theory.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4d36281829074bf3844ad192f3430b27
work_keys_str_mv	AT fujunwang anovelfeatureselectionalgorithmbasedondampingoscillationtheory AT xingwang anovelfeatureselectionalgorithmbasedondampingoscillationtheory AT fujunwang novelfeatureselectionalgorithmbasedondampingoscillationtheory AT xingwang novelfeatureselectionalgorithmbasedondampingoscillationtheory
_version_	1718374282590945280

A novel feature selection algorithm based on damping oscillation theory.

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