Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model

Ahmed A. Ewees, Mohammed A. A. Al-qaness, Laith Abualigah, Diego Oliva, Zakariya Yahya Algamal, Ahmed M. Anter, Rehab Ali Ibrahim, Rania M. Ghoniem and Mohamed Abd Elaziz
Additional contact information
Ahmed A. Ewees: Department of Computer, Damietta University, Damietta 34517, Egypt
Mohammed A. A. Al-qaness: State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Laith Abualigah: Faculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, Jordan
Diego Oliva: Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Department of Computer Sciences, Universidad de Guadalajara, Guadalajara 44430, Mexico
Zakariya Yahya Algamal: Department of Statistics and Informatics, University of Mosul, Mosul 41002, Iraq
Ahmed M. Anter: Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni Suef 62511, Egypt
Rehab Ali Ibrahim: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
Rania M. Ghoniem: Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 84428, Saudi Arabia
Mohamed Abd Elaziz: Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt

Mathematics, 2021, vol. 9, issue 18, 1-22

Abstract: Feature selection is a well-known prepossessing procedure, and it is considered a challenging problem in many domains, such as data mining, text mining, medicine, biology, public health, image processing, data clustering, and others. This paper proposes a novel feature selection method, called AOAGA, using an improved metaheuristic optimization method that combines the conventional Arithmetic Optimization Algorithm (AOA) with the Genetic Algorithm (GA) operators. The AOA is a recently proposed optimizer; it has been employed to solve several benchmark and engineering problems and has shown a promising performance. The main aim behind the modification of the AOA is to enhance its search strategies. The conventional version suffers from weaknesses, the local search strategy, and the trade-off between the search strategies. Therefore, the operators of the GA can overcome the shortcomings of the conventional AOA. The proposed AOAGA was evaluated with several well-known benchmark datasets, using several standard evaluation criteria, namely accuracy, number of selected features, and fitness function. Finally, the results were compared with the state-of-the-art techniques to prove the performance of the proposed AOAGA method. Moreover, to further assess the performance of the proposed AOAGA method, two real-world problems containing gene datasets were used. The findings of this paper illustrated that the proposed AOAGA method finds new best solutions for several test cases, and it got promising results compared to other comparative methods published in the literature.

Keywords: feature selection; data mining; machine learning; Arithmetic Optimization Algorithm (AOA); genetic algorithm (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 (3)

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