Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study
Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/11/1929 |
| _version_ | 1797492552699478016 |
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| author | Mohammad H. Nadimi-Shahraki Shokooh Taghian Seyedali Mirjalili Laith Abualigah |
| author_facet | Mohammad H. Nadimi-Shahraki Shokooh Taghian Seyedali Mirjalili Laith Abualigah |
| author_sort | Mohammad H. Nadimi-Shahraki |
| collection | DOAJ |
| description | Medical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy. |
| first_indexed | 2024-03-10T01:05:20Z |
| format | Article |
| id | doaj.art-53ee86450ce548dab94a629275452e05 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:05:20Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-53ee86450ce548dab94a629275452e052023-11-23T14:27:02ZengMDPI AGMathematics2227-73902022-06-011011192910.3390/math10111929Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case StudyMohammad H. Nadimi-Shahraki0Shokooh Taghian1Seyedali Mirjalili2Laith Abualigah3Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, IranFaculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, IranCentre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane 4006, AustraliaFaculty of Computer Sciences and Informatics, Amman Arab University, Amman 11953, JordanMedical technological advancements have led to the creation of various large datasets with numerous attributes. The presence of redundant and irrelevant features in datasets negatively influences algorithms and leads to decreases in the performance of the algorithms. Using effective features in data mining and analyzing tasks such as classification can increase the accuracy of the results and relevant decisions made by decision-makers using them. This increase can become more acute when dealing with challenging, large-scale problems in medical applications. Nature-inspired metaheuristics show superior performance in finding optimal feature subsets in the literature. As a seminal attempt, a wrapper feature selection approach is presented on the basis of the newly proposed Aquila optimizer (AO) in this work. In this regard, the wrapper approach uses AO as a search algorithm in order to discover the most effective feature subset. S-shaped binary Aquila optimizer (SBAO) and V-shaped binary Aquila optimizer (VBAO) are two binary algorithms suggested for feature selection in medical datasets. Binary position vectors are generated utilizing S- and V-shaped transfer functions while the search space stays continuous. The suggested algorithms are compared to six recent binary optimization algorithms on seven benchmark medical datasets. In comparison to the comparative algorithms, the gained results demonstrate that using both proposed BAO variants can improve the classification accuracy on these medical datasets. The proposed algorithm is also tested on the real-dataset COVID-19. The findings testified that SBAO outperforms comparative algorithms regarding the least number of selected features with the highest accuracy.https://www.mdpi.com/2227-7390/10/11/1929transfer functionmedical datanature-inspired algorithmbinary metaheuristic algorithmfeature selection |
| spellingShingle | Mohammad H. Nadimi-Shahraki Shokooh Taghian Seyedali Mirjalili Laith Abualigah Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study Mathematics transfer function medical data nature-inspired algorithm binary metaheuristic algorithm feature selection |
| title | Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study |
| title_full | Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study |
| title_fullStr | Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study |
| title_full_unstemmed | Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study |
| title_short | Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study |
| title_sort | binary aquila optimizer for selecting effective features from medical data a covid 19 case study |
| topic | transfer function medical data nature-inspired algorithm binary metaheuristic algorithm feature selection |
| url | https://www.mdpi.com/2227-7390/10/11/1929 |
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