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

Mohamed Abd Elaziz, Laith Abualigah, Dalia Yousri, Diego Oliva, Mohammed A. A. Al-Qaness, Mohammad H. Nadimi-Shahraki, Ahmed A. Ewees, Songfeng Lu and Rehab Ali Ibrahim
Additional contact information
Mohamed Abd Elaziz: School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11183, Jordan
Dalia Yousri: Electrical Engineering Department, Faculty of Engineering, Fayoum University, Faiyum 63514, Egypt
Diego Oliva: Departamento de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Guadalajara 44430, Mexico
Mohammed A. A. Al-Qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Mohammad H. Nadimi-Shahraki: Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
Ahmed A. Ewees: Department of Computer, Damietta University, Damietta 34511, Egypt
Songfeng Lu: School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Rehab Ali Ibrahim: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Mathematics, 2021, vol. 9, issue 21, 1-17

Abstract: 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.

Keywords: soft computing; machine learning; feature selection (FS); metaheuristic (MH); atomic orbital search (AOS); dynamic opposite-based learning (DOL) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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