Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection; Mathematics; Vol. 9, iss. 21

Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection; Mathematics; Vol. 9, iss. 21

Bibliographische Detailangaben
Parent link:Mathematics
Vol. 9, iss. 21.— 2021.— [2786, 17 p.]
Körperschaft: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Weitere Verfasser: Mohamed Abd Elaziz, Laith A. Abualigah, Dalia Yo. Yousri, Oliva Navarro D. A. Diego Alberto, Mohammed A. A. Al-Qaness, Mohammad H. Nadimi-Shahraki, Ewees A. A. Ahmed, Songfeng L. Lu, Rehab A. I. Ali Ibrahim
Zusammenfassung:Title screen
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.
Sprache:Englisch
Veröffentlicht: 2021
Schlagworte:
электронный ресурс
труды учёных ТПУ
soft computing
machine learning
feature selection (FS)
metaheuristic (MH)
atomic orbital search (AOS)
dynamic opposite-based learning (DOL)
вычисления
машинное обучение
метаэвристика
атомно-орбитальные модели
Online-Zugang:https://doi.org/10.3390/math9212786
Format: Elektronisch Buchkapitel
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666030

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