## Abstract

Original language | English |
---|---|

Journal | Mathematics |

Volume | 10 |

Issue number | 11 |

DOIs | |

Publication status | Published - 2022 |

## Keywords

- binary metaheuristic algorithm
- feature selection
- medical data
- nature-inspired algorithm
- transfer function

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*Mathematics*,

*10*(11). https://doi.org/10.3390/math10111929

**Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study**. In: Mathematics. 2022 ; Vol. 10, No. 11.

}

*Mathematics*, vol. 10, no. 11. https://doi.org/10.3390/math10111929

**Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study.** / Nadimi-Shahraki, M.H.; Taghian, S.; Mirjalili, S. et al.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Binary Aquila Optimizer for Selecting Effective Features from Medical Data: A COVID-19 Case Study

AU - Nadimi-Shahraki, M.H.

AU - Taghian, S.

AU - Mirjalili, S.

AU - Abualigah, L.

N1 - Export Date: 11 July 2022 Correspondence Address: Nadimi-Shahraki, M.H.; Faculty of Computer Engineering, Iran; email: nadimi@iaun.ac.ir Correspondence Address: Mirjalili, S.; Centre for Artificial Intelligence Research and Optimisation, Australia; email: ali.mirjalili@gmail.com References: Guyon, I., Elisseeff, A., An introduction to variable and feature selection (2003) J. Mach. Learn. Res, 3, pp. 1157-1182; Liu, H., Motoda, H., (2012) Feature Selection for Knowledge Discovery and Data Mining, 454. , Springer Science & Business Media: Berlin/Hei-delberg, Germany; Kohavi, R., John, G.H., Wrappers for feature subset selection (1997) Artif. 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PY - 2022

Y1 - 2022

N2 - 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 out-performs comparative algorithms regarding the least number of selected features with the highest accuracy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

AB - 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 out-performs comparative algorithms regarding the least number of selected features with the highest accuracy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

KW - binary metaheuristic algorithm

KW - feature selection

KW - medical data

KW - nature-inspired algorithm

KW - transfer function

U2 - 10.3390/math10111929

DO - 10.3390/math10111929

M3 - Article

VL - 10

JO - Mathematics

JF - Mathematics

SN - 2227-7390

IS - 11

ER -