Nested ensemble selection: An effective hybrid feature selection method
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
| Заглавие |
Nested ensemble selection: An effective hybrid feature selection method
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| Автор |
Kamalov, F.
Sulieman, H. Moussa, S. Reyes, J. A. Safaraliev, M. |
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| Тематика |
ENSEMBLE SELECTION
FEATURE SELECTION FILTER METHOD MACHINE LEARNING RANDOM FOREST SYNTHETIC DATA WRAPPER METHOD |
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| Описание |
It 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)
American University of Sharjah, AUS The work in this paper was supported by the Open Access Program from the American University of Sharjah. |
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| Дата |
2024-04-05T16:32:36Z
2024-04-05T16:32:36Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Kamalov, F, Sulieman, H, Moussa, S, Reyes, JA & Safaraliev, M 2023, 'Nested ensemble selection: An effective hybrid feature selection method', Heliyon, Том. 9, № 9, стр. e19686. https://doi.org/10.1016/j.heliyon.2023.e19686
Kamalov, 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 2405-8440 Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171388549&doi=10.1016%2fj.heliyon.2023.e19686&partnerID=40&md5=069477ff41a059ac197571ab82781de5 http://www.cell.com/article/S2405844023068949/pdf http://elar.urfu.ru/handle/10995/130780 10.1016/j.heliyon.2023.e19686 85171388549 001140561100001 |
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| Язык |
en
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Elsevier Ltd
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| Источник |
Heliyon
Heliyon |
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