BukaGini: A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance

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dc.authoridSalah, Bashir/0000-0003-2709-760X
dc.authoridBouke, Mohamed Aly/0000-0003-3264-601X
dc.authoridFrnda, Jaroslav/0000-0001-6065-3087
dc.authoridCengiz, Korhan/0000-0001-6594-8861
dc.authorwosidSalah, Bashir/ABC-5845-2020
dc.authorwosidBouke, Mohamed Aly/HZJ-0305-2023
dc.authorwosidFrnda, Jaroslav/M-1454-2019
dc.contributor.authorBouke, Mohamed Aly
dc.contributor.authorAbdullah, Azizol
dc.contributor.authorFrnda, Jaroslav
dc.contributor.authorCengiz, Korhan
dc.contributor.authorSalah, Bashir
dc.date.accessioned2024-05-19T14:39:41Z
dc.date.available2024-05-19T14:39:41Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractFeature interaction is a vital aspect of Machine Learning (ML) algorithms, and gaining a deep understanding of these interactions can significantly enhance model performance. This paper introduces the BukaGini algorithm, an innovative and robust approach for feature interaction analysis that capitalizes on the Gini impurity index. By exploiting the unique properties of the BukaGini index, our proposed algorithm effectively captures both linear and nonlinear feature interactions, providing a richer and more comprehensive representation of the underlying data. We thoroughly evaluate the BukaGini algorithm against traditional Gini index-based methods on various real-world datasets. These datasets include the High School Students' Performance (HSSP) dataset, which examines factors affecting student performance; Cancer Data, which focuses on identifying cancer types based on gene expression; Spambase, which targets spam email classification; and the UNSW-NB15 dataset, which addresses network intrusion detection. Our experimental results demonstrate that the BukaGini algorithm consistently outperforms traditional Gini index-based methods in terms of accuracy. Across the tested datasets, the BukaGini algorithm achieves improvements ranging from 0.32% to 2.50%, underscoring its effectiveness in handling diverse data types and problem domains. This performance gain highlights the potential of the BukaGini algorithm as a valuable tool for feature interaction analysis in various ML applications.en_US
dc.description.sponsorshipKing Saud University, Saudi Arabia [RSP2023R145]; Ministry of Education, Youth and Sports of the Czech Republic [2/KKMHI/2022]; Institutional research of the Faculty of Operation and Economics of Transport and Communications-University of Zilina; [SP2023/007]en_US
dc.description.sponsorshipThis study received funding from King Saud University, Saudi Arabia through researchers supporting project number (RSP2023R145). And was supported by the Ministry of Education, Youth and Sports of the Czech Republic under the grant SP2023/007 conducted by VSB -Technical University of Ostrava, Czechia, and partially supported by Institutional research of the Faculty of Operation and Economics of Transport and Communications-University of Zilina, no. 2/KKMHI/2022.& nbsp;en_US
dc.identifier.doi10.1109/ACCESS.2023.3284975
dc.identifier.endpage59396en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85162645716en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage59386en_US
dc.identifier.urihttps://doi.org10.1109/ACCESS.2023.3284975
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4829
dc.identifier.volume11en_US
dc.identifier.wosWOS:001016812900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectBukagini Algorithmen_US
dc.subjectGini Indexen_US
dc.subjectEnsemble Learningen_US
dc.subjectFeature Interaction Analysisen_US
dc.subjectData Miningen_US
dc.titleBukaGini: A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performanceen_US
dc.typeArticleen_US

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