Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model; Mathematics; Vol. 9, iss. 18
| Parent link: | Mathematics Vol. 9, iss. 18.— 2021.— [2321, 22 p.] |
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| Korporativní autor: | |
| Další autoři: | , , , , , , , , |
| Shrnutí: | Title screen 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. |
| Jazyk: | angličtina |
| Vydáno: |
2021
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| Témata: | |
| On-line přístup: | https://doi.org/10.3390/math9182321 |
| Médium: | MixedMaterials Elektronický zdroj Kapitola |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=667703 |
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| 200 | 1 | |a Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model |f A. A. Ewees, M. A. A. Al-qaness, L. Abualigah [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 75 tit.] | ||
| 330 | |a 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. | ||
| 461 | |t Mathematics | ||
| 463 | |t Vol. 9, iss. 18 |v [2321, 22 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a feature selection | |
| 610 | 1 | |a data mining | |
| 610 | 1 | |a machine learning | |
| 610 | 1 | |a Arithmetic Optimization Algorithm (AOA) | |
| 610 | 1 | |a genetic algorithm | |
| 610 | 1 | |a функции | |
| 610 | 1 | |a сбор данных | |
| 610 | 1 | |a машинное обучение | |
| 610 | 1 | |a алгоритмы | |
| 610 | 1 | |a оптимизация | |
| 610 | 1 | |a генетические алгоритмы | |
| 610 | 1 | |a признаки | |
| 610 | 1 | |a риски | |
| 701 | 1 | |a Ewees |b A. A. |g Ahmed | |
| 701 | 1 | |a Al-qaness |b M. A. A. |g Mohammed | |
| 701 | 1 | |a Abualigah |b L. |g Laith | |
| 701 | 1 | |a Oliva Navarro |b D. A. |c specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1983- |g Diego Alberto |3 (RuTPU)RU\TPU\pers\37366 | |
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| 701 | 1 | |a Ali |b I. R. |g Ibrahim Rehab | |
| 701 | 1 | |a Ghoniem |b R. M. |g Rania | |
| 701 | 1 | |a Mokhamed Elsaed (Mohamed Abd Elaziz) |b A. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1987- |g Akhmed Mokhamed |3 (RuTPU)RU\TPU\pers\46943 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
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