An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex...

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Autores principales:	Passent El Kafrawy, Hanaa Fathi, Mohammed Qaraad, Ayda K. Kelany, Xumin Chen
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Cancer classification feature selection genomic microarray data support vector machine ensemble minimum redundancy > maximum relevance Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/fc9ce6eb64744b63be5363e9993b5579
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spelling	oai:doaj.org-article:fc9ce6eb64744b63be5363e9993b55792021-11-26T00:01:29ZAn Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data2169-353610.1109/ACCESS.2021.3123090https://doaj.org/article/fc9ce6eb64744b63be5363e9993b55792021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9585477/https://doaj.org/toc/2169-3536Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe), the proposed model provides a framework for combining filter-based, ensemble, and embedded methods to select the most relevant and informative genes from high-dimensional microarray data by fusing embedded SVM coefficients (features ranking) with ensemble mRMRe. Eight of the most commonly used microarray datasets for various types of cancer were used to evaluate the model. The selected subset feature is evaluated by four different types of classifiers: random forest (RF), multilayer perceptron (MLP), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). The experimental results show that the proposed model reduces time consumption and dimensionality and improves the differentiation of cancer tissues from benign tissues. Furthermore, the selected genes for the brain cancer dataset are biologically interpreted, and it agrees with the findings of relevant biomedical studies and plays an important role in patient prognosis.Passent El KafrawyHanaa FathiMohammed QaraadAyda K. KelanyXumin ChenIEEEarticleCancer classificationfeature selectiongenomic microarray datasupport vector machineensemble minimum redundancy--maximum relevanceElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 155353-155369 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Cancer classification feature selection genomic microarray data support vector machine ensemble minimum redundancy--maximum relevance Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Cancer classification feature selection genomic microarray data support vector machine ensemble minimum redundancy--maximum relevance Electrical engineering. Electronics. Nuclear engineering TK1-9971 Passent El Kafrawy Hanaa Fathi Mohammed Qaraad Ayda K. Kelany Xumin Chen An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
description	Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe), the proposed model provides a framework for combining filter-based, ensemble, and embedded methods to select the most relevant and informative genes from high-dimensional microarray data by fusing embedded SVM coefficients (features ranking) with ensemble mRMRe. Eight of the most commonly used microarray datasets for various types of cancer were used to evaluate the model. The selected subset feature is evaluated by four different types of classifiers: random forest (RF), multilayer perceptron (MLP), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). The experimental results show that the proposed model reduces time consumption and dimensionality and improves the differentiation of cancer tissues from benign tissues. Furthermore, the selected genes for the brain cancer dataset are biologically interpreted, and it agrees with the findings of relevant biomedical studies and plays an important role in patient prognosis.
format	article
author	Passent El Kafrawy Hanaa Fathi Mohammed Qaraad Ayda K. Kelany Xumin Chen
author_facet	Passent El Kafrawy Hanaa Fathi Mohammed Qaraad Ayda K. Kelany Xumin Chen
author_sort	Passent El Kafrawy
title	An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
title_short	An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
title_full	An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
title_fullStr	An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
title_full_unstemmed	An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data
title_sort	efficient svm-based feature selection model for cancer classification using high-dimensional microarray data
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/fc9ce6eb64744b63be5363e9993b5579
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An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

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