Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexit...

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Autores principales:	Mohamed Abd Elaziz, Laith Abualigah, Dalia Yousri, Diego Oliva, Mohammed A. A. Al-Qaness, Mohammad H. Nadimi-Shahraki, Ahmed A. Ewees, Songfeng Lu, Rehab Ali Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	soft computing machine learning feature selection (FS) metaheuristic (MH) atomic orbital search (AOS) dynamic opposite-based learning (DOL) Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/4ce1691c716d49c5b5c383a07d1c4110
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id	oai:doaj.org-article:4ce1691c716d49c5b5c383a07d1c4110
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spelling	oai:doaj.org-article:4ce1691c716d49c5b5c383a07d1c41102021-11-11T18:19:34ZBoosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection10.3390/math92127862227-7390https://doaj.org/article/4ce1691c716d49c5b5c383a07d1c41102021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/21/2786https://doaj.org/toc/2227-7390Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.Mohamed Abd ElazizLaith AbualigahDalia YousriDiego OlivaMohammed A. A. Al-QanessMohammad H. Nadimi-ShahrakiAhmed A. EweesSongfeng LuRehab Ali IbrahimMDPI AGarticlesoft computingmachine learningfeature selection (FS)metaheuristic (MH)atomic orbital search (AOS)dynamic opposite-based learning (DOL)MathematicsQA1-939ENMathematics, Vol 9, Iss 2786, p 2786 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	soft computing machine learning feature selection (FS) metaheuristic (MH) atomic orbital search (AOS) dynamic opposite-based learning (DOL) Mathematics QA1-939
spellingShingle	soft computing machine learning feature selection (FS) metaheuristic (MH) atomic orbital search (AOS) dynamic opposite-based learning (DOL) Mathematics QA1-939 Mohamed Abd Elaziz Laith Abualigah Dalia Yousri Diego Oliva Mohammed A. A. Al-Qaness Mohammad H. Nadimi-Shahraki Ahmed A. Ewees Songfeng Lu Rehab Ali Ibrahim Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
description	Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.
format	article
author	Mohamed Abd Elaziz Laith Abualigah Dalia Yousri Diego Oliva Mohammed A. A. Al-Qaness Mohammad H. Nadimi-Shahraki Ahmed A. Ewees Songfeng Lu Rehab Ali Ibrahim
author_facet	Mohamed Abd Elaziz Laith Abualigah Dalia Yousri Diego Oliva Mohammed A. A. Al-Qaness Mohammad H. Nadimi-Shahraki Ahmed A. Ewees Songfeng Lu Rehab Ali Ibrahim
author_sort	Mohamed Abd Elaziz
title	Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
title_short	Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
title_full	Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
title_fullStr	Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
title_full_unstemmed	Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection
title_sort	boosting atomic orbit search using dynamic-based learning for feature selection
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/4ce1691c716d49c5b5c383a07d1c4110
work_keys_str_mv	AT mohamedabdelaziz boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT laithabualigah boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT daliayousri boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT diegooliva boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT mohammedaaalqaness boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT mohammadhnadimishahraki boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT ahmedaewees boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT songfenglu boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection AT rehabaliibrahim boostingatomicorbitsearchusingdynamicbasedlearningforfeatureselection
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Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection

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