ArticleKnowledge-based systems2022
BCOVIDOA: A Novel Binary Coronavirus Disease Optimization Algorithm for Feature Selection.
Article in Knowledge-based systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Enhanced generalized normal distribution optimizer with Gaussian distribution repair method and cauchy reverse learning for features selection.Scientific reports · 2026Article
- A multistrategy improved hunger games search algorithm.Scientific reports · 2025Article
- Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification.Scientific reports · 2024Article
- Article
- Multilevel Threshold Segmentation of Skin Lesions in Color Images Using Coronavirus Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- PSO-Incorporated Hybrid Artificial Hummingbird Algorithm with Elite Opposition-Based Learning and Cauchy Mutation: A Case Study of Shape Optimization for CSGC-Ball Curves.Biomimetics (Basel, Switzerland) · 2023Article
- Binary Sand Cat Swarm Optimization Algorithm for Wrapper Feature Selection on Biological Data.Biomimetics (Basel, Switzerland) · 2023Article
- Human Activity Recognition Using Hybrid Coronavirus Disease Optimization Algorithm for Internet of Medical Things.Sensors (Basel, Switzerland) · 2023Article
- Multilevel thresholding satellite image segmentation using chaotic coronavirus optimization algorithm with hybrid fitness function.Neural computing & applications · 2023Article
- Differential evolution and particle swarm optimization against COVID-19.Artificial intelligence review · 2022Article
- An Intelligent Optimization Algorithm for Constructing a DNA Storage Code: NOL-HHO.International journal of molecular sciences · 2020Article
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4 authors.
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Abstract
The increased use of digital tools such as smart phones, Internet of Things devices, cameras, and microphones, has led to the produuction of big data. Large data dimensionality, redundancy, and irrelevance are inherent challenging problems when it comes to big data. Feature selection is a necessary process to select the optimal subset of features when addressing such problems. In this paper, the authors propose a novel Binary Coronavirus Disease Optimization Algorithm (BCOVIDOA) for feature selection, where the Coronavirus Disease Optimization Algorithm (COVIDOA) is a new optimization technique that mimics the replication mechanism used by Coronavirus when hijacking human cells. The performance of the proposed algorithm is evaluated using twenty-six standard benchmark datasets from UCI Repository. The results are compared with nine recent wrapper feature selection algorithms. The experimental results demonstrate that the proposed BCOVIDOA significantly outperforms the existing algorithms in terms of accuracy, best cost, the average cost (AVG), standard deviation (STD), and size of selected features. Additionally, the Wilcoxon rank-sum test is calculated to prove the statistical significance of the results.
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