Nested ensemble selection: An effective hybrid feature selection method

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorSulieman, Hana
dc.contributor.authorMoussa, Sherif
dc.contributor.authorReyes, Jorge Avante
dc.contributor.authorSafaraliev, Murodbek
dc.date.accessioned2023-10-19T05:42:09Z
dc.date.available2023-10-19T05:42:09Z
dc.date.copyright© 2023
dc.date.issued2023-09
dc.description.abstractIt has been shown that while feature selection algorithms are able to distinguish between relevant and irrelevant features, they fail to differentiate between relevant and redundant and correlated features. To address this issue, we propose a highly effective approach, called Nested Ensemble Selection (NES), that is based on a combination of filter and wrapper methods. The proposed feature selection algorithm differs from the existing filter-wrapper hybrid methods in its simplicity and efficiency as well as precision. The new algorithm is able to separate the relevant variables from the irrelevant as well as the redundant and correlated features. Furthermore, we provide a robust heuristic for identifying the optimal number of selected features which remains one of the greatest challenges in feature selection. Numerical experiments on synthetic and real-life data demonstrate the effectiveness of the proposed method. The NES algorithm achieves perfect precision on the synthetic data and near optimal accuracy on the real-life data. The proposed method is compared against several popular algorithms including mRMR, Boruta, genetic, recursive feature elimination, Lasso, and Elastic Net. The results show that NES significantly outperforms the benchmarks algorithms especially on multi-class datasets. © 2023 The Author(s)
dc.identifier.citationKamalov, F., Sulieman, H., Moussa, S., Reyes, J. A., & Safaraliev, M. (2023). Nested ensemble selection: An effective hybrid feature selection method. Heliyon, 9(9), e19686. https://doi.org/10.1016/j.heliyon.2023.e19686
dc.identifier.issn24058440
dc.identifier.urihttps://doi.org/10.1016/j.heliyon.2023.e19686
dc.identifier.urihttps://hdl.handle.net/20.500.12519/934
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofseriesHeliyon; Volume 9, Issue 9
dc.rightsCreative Commons Attribution (CC BY 4.0)
dc.rights.holderCopyright : © 2023 The Author(s)
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEnsemble selection
dc.subjectFeature selection
dc.subjectFilter method
dc.subjectMachine learning
dc.subjectRandom forest
dc.subjectSynthetic data
dc.subjectWrapper method
dc.titleNested ensemble selection: An effective hybrid feature selection method
dc.typeArticle

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