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

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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https://doaj.org/article/4ce1691c716d49c5b5c383a07d1c4110

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