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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Veröffentlicht in:Mathematics (Basel) Jg. 10; H. 11; S. 1929
Hauptverfasser: Nadimi-Shahraki, Mohammad H., Taghian, Shokooh, Mirjalili, Seyedali, Abualigah, Laith
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.06.2022
Schlagworte:
Algorithms
binary metaheuristic algorithm
Classification
Coronaviruses
COVID-19
Data mining
Datasets
Decision making
Engineering
Evolution
Feature selection
Genetic algorithms
Heuristic methods
Mathematical optimization
Mathematics
medical data
nature-inspired algorithm
Optimization
Optimization algorithms
Search algorithms
transfer function
Transfer functions
Germany
ISSN:2227-7390, 2227-7390
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math10111929