Majority scoring with backward elimination in PLS for high dimensional spectrum data

Abstract Variable selection is crucial issue for high dimensional data modeling, where sample size is smaller compared to number of variables. Recently, majority scoring of filter measures in PLS (MS-PLS) is introduced for variable selection in high dimensional data. Filter measures are not greedy f...

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Autor principal:	Freeh N. Alenezi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/dc55cfc4d44f4cbdb4a9bee95682efe4
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spelling	oai:doaj.org-article:dc55cfc4d44f4cbdb4a9bee95682efe42021-12-02T18:51:53ZMajority scoring with backward elimination in PLS for high dimensional spectrum data10.1038/s41598-021-96389-22045-2322https://doaj.org/article/dc55cfc4d44f4cbdb4a9bee95682efe42021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-96389-2https://doaj.org/toc/2045-2322Abstract Variable selection is crucial issue for high dimensional data modeling, where sample size is smaller compared to number of variables. Recently, majority scoring of filter measures in PLS (MS-PLS) is introduced for variable selection in high dimensional data. Filter measures are not greedy for optimal performance, hence we have proposed majority scoring with backward elimination in PLS (MSBE-PLS). In MSBE-PLS we have considered variable importance on projection (VIP) and selectivity ratio (SR). In each iteration of backward elimination in PLS variables are considered influential if they were selected by both filter indicator. The proposed method is implemented for corn’s and diesel’s content prediction. The corn contents include protein, oil, starch and moisture while diesel contents include boiling point at 50% recovery, cetane number, density, freezing temperature of the fuel, total aromatics, and viscosity. The proposed method outperforms in terms of RMSE when compared with reference methods. In addition to validating the spectrum models, data properties are also examined for explaining prediction behaviors. Moreover, MSBE-PLS select the moderate number of influential variables, hence it presents the parsimonious model for predicting contents based on spectrum data.Freeh N. AleneziNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Freeh N. Alenezi Majority scoring with backward elimination in PLS for high dimensional spectrum data
description	Abstract Variable selection is crucial issue for high dimensional data modeling, where sample size is smaller compared to number of variables. Recently, majority scoring of filter measures in PLS (MS-PLS) is introduced for variable selection in high dimensional data. Filter measures are not greedy for optimal performance, hence we have proposed majority scoring with backward elimination in PLS (MSBE-PLS). In MSBE-PLS we have considered variable importance on projection (VIP) and selectivity ratio (SR). In each iteration of backward elimination in PLS variables are considered influential if they were selected by both filter indicator. The proposed method is implemented for corn’s and diesel’s content prediction. The corn contents include protein, oil, starch and moisture while diesel contents include boiling point at 50% recovery, cetane number, density, freezing temperature of the fuel, total aromatics, and viscosity. The proposed method outperforms in terms of RMSE when compared with reference methods. In addition to validating the spectrum models, data properties are also examined for explaining prediction behaviors. Moreover, MSBE-PLS select the moderate number of influential variables, hence it presents the parsimonious model for predicting contents based on spectrum data.
format	article
author	Freeh N. Alenezi
author_facet	Freeh N. Alenezi
author_sort	Freeh N. Alenezi
title	Majority scoring with backward elimination in PLS for high dimensional spectrum data
title_short	Majority scoring with backward elimination in PLS for high dimensional spectrum data
title_full	Majority scoring with backward elimination in PLS for high dimensional spectrum data
title_fullStr	Majority scoring with backward elimination in PLS for high dimensional spectrum data
title_full_unstemmed	Majority scoring with backward elimination in PLS for high dimensional spectrum data
title_sort	majority scoring with backward elimination in pls for high dimensional spectrum data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dc55cfc4d44f4cbdb4a9bee95682efe4
work_keys_str_mv	AT freehnalenezi majorityscoringwithbackwardeliminationinplsforhighdimensionalspectrumdata
_version_	1718377416911486976

Majority scoring with backward elimination in PLS for high dimensional spectrum data

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