A Hyper Learning Binary Dragonfly Algorithm for Feature Selection: A COVID-19 Case Study

The rapid expansion of information science has caused the issue of “the curse of dimensionality”, which will negatively affect the performance of the machine learning model. Feature selection is typically considered as a pre-processing mechanism to find an optimal subset of features from a given set...

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Veröffentlicht in:Knowledge-based systems Jg. 212; S. 106553
Hauptverfasser: Too, Jingwei, Mirjalili, Seyedali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Amsterdam Elsevier B.V 05.01.2021
Elsevier Science Ltd
Schlagworte:
Algorithm
Algorithms
Artificial Intelligence
Binary Dragonfly Algorithm
Binary Optimization
Case studies
Classification
Combinatorial Optimization
Coronaviruses
COVID-19
Data mining
Datasets
Feature selection
Information retrieval
Information science
Learning
Learning strategies
Machine learning
Novels
Optimization
Particle Swarm Optimization
Viral diseases
Combinatorial Optimization
Feature selection
Binary Dragonfly Algorithm
Artificial Intelligence
Classification
Data mining
Algorithm
Optimization
Particle Swarm Optimization
Binary Optimization
ISSN:0950-7051, 1872-7409
Online-Zugang:Volltext
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Zusammenfassung:The rapid expansion of information science has caused the issue of “the curse of dimensionality”, which will negatively affect the performance of the machine learning model. Feature selection is typically considered as a pre-processing mechanism to find an optimal subset of features from a given set of all features in the data mining process. In this article, a novel Hyper Learning Binary Dragonfly Algorithm (HLBDA) is proposed as a wrapper-based method to find an optimal subset of features for a given classification problem. HLBDA is an enhanced version of the Binary Dragonfly Algorithm (BDA) in which a hyper learning strategy is used to assist the algorithm to escape local optima and improve searching behavior. The proposed HLBDA is compared with eight algorithms in the literature. Several assessment indicators are employed to evaluate and compare the effectiveness of these methods over twenty-one datasets from the University of California Irvine (UCI) repository and Arizona State University. Also, the proposed method is applied to a coronavirus disease (COVID-19) dataset. The results demonstrate the superiority of HLBDA in increasing classification accuracy and reducing the number of selected features.22The source code of HLBDA is publicly available at https://seyedalimirjalili.com/da.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.106553