B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are...

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Autores principales:	Mohammad H. Nadimi-Shahraki, Mahdis Banaie-Dezfouli, Hoda Zamani, Shokooh Taghian, Seyedali Mirjalili
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optimization binary metaheuristic algorithms swarm intelligence algorithms feature selection medical datasets transfer function Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/b114659a5c654ecdb3646531bfc5b459
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spelling	oai:doaj.org-article:b114659a5c654ecdb3646531bfc5b4592021-11-25T17:17:21ZB-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets10.3390/computers101101362073-431Xhttps://doaj.org/article/b114659a5c654ecdb3646531bfc5b4592021-10-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/136https://doaj.org/toc/2073-431XAdvancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.Mohammad H. Nadimi-ShahrakiMahdis Banaie-DezfouliHoda ZamaniShokooh TaghianSeyedali MirjaliliMDPI AGarticleoptimizationbinary metaheuristic algorithmsswarm intelligence algorithmsfeature selectionmedical datasetstransfer functionElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 136, p 136 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optimization binary metaheuristic algorithms swarm intelligence algorithms feature selection medical datasets transfer function Electronic computers. Computer science QA75.5-76.95
spellingShingle	optimization binary metaheuristic algorithms swarm intelligence algorithms feature selection medical datasets transfer function Electronic computers. Computer science QA75.5-76.95 Mohammad H. Nadimi-Shahraki Mahdis Banaie-Dezfouli Hoda Zamani Shokooh Taghian Seyedali Mirjalili B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
description	Advancements in medical technology have created numerous large datasets including many features. Usually, all captured features are not necessary, and there are redundant and irrelevant features, which reduce the performance of algorithms. To tackle this challenge, many metaheuristic algorithms are used to select effective features. However, most of them are not effective and scalable enough to select effective features from large medical datasets as well as small ones. Therefore, in this paper, a binary moth-flame optimization (B-MFO) is proposed to select effective features from small and large medical datasets. Three categories of B-MFO were developed using S-shaped, V-shaped, and U-shaped transfer functions to convert the canonical MFO from continuous to binary. These categories of B-MFO were evaluated on seven medical datasets and the results were compared with four well-known binary metaheuristic optimization algorithms: BPSO, bGWO, BDA, and BSSA. In addition, the convergence behavior of the B-MFO and comparative algorithms were assessed, and the results were statistically analyzed using the Friedman test. The experimental results demonstrate a superior performance of B-MFO in solving the feature selection problem for different medical datasets compared to other comparative algorithms.
format	article
author	Mohammad H. Nadimi-Shahraki Mahdis Banaie-Dezfouli Hoda Zamani Shokooh Taghian Seyedali Mirjalili
author_facet	Mohammad H. Nadimi-Shahraki Mahdis Banaie-Dezfouli Hoda Zamani Shokooh Taghian Seyedali Mirjalili
author_sort	Mohammad H. Nadimi-Shahraki
title	B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
title_short	B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
title_full	B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
title_fullStr	B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
title_full_unstemmed	B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets
title_sort	b-mfo: a binary moth-flame optimization for feature selection from medical datasets
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b114659a5c654ecdb3646531bfc5b459
work_keys_str_mv	AT mohammadhnadimishahraki bmfoabinarymothflameoptimizationforfeatureselectionfrommedicaldatasets AT mahdisbanaiedezfouli bmfoabinarymothflameoptimizationforfeatureselectionfrommedicaldatasets AT hodazamani bmfoabinarymothflameoptimizationforfeatureselectionfrommedicaldatasets AT shokoohtaghian bmfoabinarymothflameoptimizationforfeatureselectionfrommedicaldatasets AT seyedalimirjalili bmfoabinarymothflameoptimizationforfeatureselectionfrommedicaldatasets
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B-MFO: A Binary Moth-Flame Optimization for Feature Selection from Medical Datasets

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