Nested ensemble selection: An effective hybrid feature selection method
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 Selecti...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , |
| التنسيق: | article |
| منشور في: |
2023
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32531 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513442478555136 |
|---|---|
| author | Kamalov, Firuz |
| author2 | Sulieman, Hana Moussa, Sherif Reyes, Jorge Avante Safaraliev, Murodbek |
| author2_role | author author author author |
| author_facet | Kamalov, Firuz Sulieman, Hana Moussa, Sherif Reyes, Jorge Avante Safaraliev, Murodbek |
| author_role | author |
| dc.creator.none.fl_str_mv | Kamalov, Firuz Sulieman, Hana Moussa, Sherif Reyes, Jorge Avante Safaraliev, Murodbek |
| dc.date.none.fl_str_mv | 2023 2025-12-08T07:21:35Z 2025-12-08T07:21:35Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | Firuz Kamalov, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes, Murodbek Safaraliev, Nested ensemble selection: An effective hybrid feature selection method, Heliyon, Volume 9, Issue 9, 2023, e19686, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2023.e19686. 2405-8440 https://hdl.handle.net/11073/32531 10.1016/j.heliyon.2023.e19686 |
| dc.language.none.fl_str_mv | en_US |
| dc.publisher.none.fl_str_mv | ScienceDirect |
| dc.relation.none.fl_str_mv | https://doi.org/10.1016/j.heliyon.2023.e19686 |
| dc.subject.none.fl_str_mv | Feature selection Ensemble selection Random forest Synthetic data Machine learning Filter method Wrapper method |
| dc.title.none.fl_str_mv | Nested ensemble selection: An effective hybrid feature selection method |
| dc.type.none.fl_str_mv | Peer-Reviewed Published version info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article |
| description | 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. |
| format | article |
| id | aus_bba83eb3c2e261c7756c0a40a099a57b |
| identifier_str_mv | Firuz Kamalov, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes, Murodbek Safaraliev, Nested ensemble selection: An effective hybrid feature selection method, Heliyon, Volume 9, Issue 9, 2023, e19686, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2023.e19686. 2405-8440 10.1016/j.heliyon.2023.e19686 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32531 |
| publishDate | 2023 |
| publisher.none.fl_str_mv | ScienceDirect |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Nested ensemble selection: An effective hybrid feature selection methodKamalov, FiruzSulieman, HanaMoussa, SherifReyes, Jorge AvanteSafaraliev, MurodbekFeature selectionEnsemble selectionRandom forestSynthetic dataMachine learningFilter methodWrapper methodIt 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.American University of SharjahScienceDirect2025-12-08T07:21:35Z2025-12-08T07:21:35Z2023Peer-ReviewedPublished versioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfFiruz Kamalov, Hana Sulieman, Sherif Moussa, Jorge Avante Reyes, Murodbek Safaraliev, Nested ensemble selection: An effective hybrid feature selection method, Heliyon, Volume 9, Issue 9, 2023, e19686, ISSN 2405-8440, https://doi.org/10.1016/j.heliyon.2023.e19686.2405-8440https://hdl.handle.net/11073/3253110.1016/j.heliyon.2023.e19686en_UShttps://doi.org/10.1016/j.heliyon.2023.e19686oai:repository.aus.edu:11073/325312025-12-08T11:41:47Z |
| spellingShingle | Nested ensemble selection: An effective hybrid feature selection method Kamalov, Firuz Feature selection Ensemble selection Random forest Synthetic data Machine learning Filter method Wrapper method |
| status_str | publishedVersion |
| title | Nested ensemble selection: An effective hybrid feature selection method |
| title_full | Nested ensemble selection: An effective hybrid feature selection method |
| title_fullStr | Nested ensemble selection: An effective hybrid feature selection method |
| title_full_unstemmed | Nested ensemble selection: An effective hybrid feature selection method |
| title_short | Nested ensemble selection: An effective hybrid feature selection method |
| title_sort | Nested ensemble selection: An effective hybrid feature selection method |
| topic | Feature selection Ensemble selection Random forest Synthetic data Machine learning Filter method Wrapper method |
| url | https://hdl.handle.net/11073/32531 |