HBDFA: An intelligent nature-inspired computing with high-dimensional data analytics

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dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.contributor.authorDinc, Baris
dc.contributor.authorKaya, Yasin
dc.date.accessioned2025-01-06T17:36:39Z
dc.date.available2025-01-06T17:36:39Z
dc.date.issued2024
dc.description.abstractThe rapid development of data science has led to the emergence of high-dimensional datasets in machine learning. The curse of dimensionality is a significant problem caused by high-dimensional data with a small sample size. This paper proposes a novel hybrid binary dragonfly algorithm (HBDFA) in which a distance-based similarity evaluation algorithm is embedded before the dragonfly algorithm (DA) searching behavior to select the most discriminating features. The two-step feature selection mechanism of HBDFA enables the method to explore the feature space reduced by the distance-based similarity evaluation algorithm. The model was evaluated on two datasets. The first dataset contained 200 reports from 4 evenly distributed categories of Daily Mail Online: COVID-19, economy, science, and sports. The second dataset was the publicly available Spam dataset. The proposed model is compared with binary versions of four popular metaheuristic algorithms. The model achieved an accuracy rate of 96.75% by reducing 66.5% of the top 100 features determined on the first dataset. Results on the Spam dataset reveal that HBDFA gives the best classification results with over 95% accuracy. The experimental results show the superiority of HBDFA in searching high-dimensional data, improving classification results, and reducing the number of selected features.
dc.identifier.doi10.1007/s11042-023-16039-9
dc.identifier.endpage11592
dc.identifier.issn1380-7501
dc.identifier.issn1573-7721
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85163674755
dc.identifier.scopusqualityQ1
dc.identifier.startpage11573
dc.identifier.urihttps://doi.org/10.1007/s11042-023-16039-9
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1938
dc.identifier.volume83
dc.identifier.wosWOS:001022136100014
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofMultimedia Tools and Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectBinary dragonfly algorithm
dc.subjectCOVID-19
dc.subjectText mining
dc.subjectNature inspired algorithms
dc.subjectFeature selection
dc.titleHBDFA: An intelligent nature-inspired computing with high-dimensional data analytics
dc.typeArticle

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