A synergy Thompson sampling hyper‐heuristic for the feature selection problem.
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| Title: | A synergy Thompson sampling hyper‐heuristic for the feature selection problem. |
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| Authors: | Lassouaoui, Mourad1 (AUTHOR) lassouaoui.mourad@gmail.com, Boughaci, Dalila1 (AUTHOR), Benhamou, Belaid2 (AUTHOR) |
| Source: | Computational Intelligence. Jun2022, Vol. 38 Issue 3, p1083-1105. 23p. |
| Subjects: | Feature selection, Reinforcement learning, NP-hard problems, Metaheuristic algorithms, Combinatorial optimization, Machine learning |
| Abstract: | Summary: To classify high‐dimensional data, feature selection plays a key role to eliminate irrelevant attributes and enhance the classification accuracy and efficiency. Since feature selection is an NP‐Hard problem, many heuristics and metaheuristics have been used to tackle in practice this problem. In this article, we propose a novel approach that consists in a probabilistic selection hyper‐heuristic called the synergy Thompson sampling hyper‐heuristic. The Thompson sampling selection strategy is a probabilistic reinforcement learning mechanism to assess the behavior of the low‐level heuristics, and to predict which one will be more efficient at each point during the search process. The proposed hyper‐heuristic is combined with a 1 nearest neighbor classifier from the Weka framework. It aims to find the best subset of features that maximizes the classification accuracy rate. Experimental results show a good performance in favor of the proposed method when comparing with other existing approaches. [ABSTRACT FROM AUTHOR] |
| Copyright of Computational Intelligence is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 157565768 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A synergy Thompson sampling hyper‐heuristic for the feature selection problem. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lassouaoui%2C+Mourad%22">Lassouaoui, Mourad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> lassouaoui.mourad@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Boughaci%2C+Dalila%22">Boughaci, Dalila</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Benhamou%2C+Belaid%22">Benhamou, Belaid</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computational+Intelligence%22">Computational Intelligence</searchLink>. Jun2022, Vol. 38 Issue 3, p1083-1105. 23p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22NP-hard+problems%22">NP-hard problems</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Combinatorial+optimization%22">Combinatorial optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Summary: To classify high‐dimensional data, feature selection plays a key role to eliminate irrelevant attributes and enhance the classification accuracy and efficiency. Since feature selection is an NP‐Hard problem, many heuristics and metaheuristics have been used to tackle in practice this problem. In this article, we propose a novel approach that consists in a probabilistic selection hyper‐heuristic called the synergy Thompson sampling hyper‐heuristic. The Thompson sampling selection strategy is a probabilistic reinforcement learning mechanism to assess the behavior of the low‐level heuristics, and to predict which one will be more efficient at each point during the search process. The proposed hyper‐heuristic is combined with a 1 nearest neighbor classifier from the Weka framework. It aims to find the best subset of features that maximizes the classification accuracy rate. Experimental results show a good performance in favor of the proposed method when comparing with other existing approaches. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computational Intelligence is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/coin.12325 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 1083 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Reinforcement learning Type: general – SubjectFull: NP-hard problems Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Combinatorial optimization Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A synergy Thompson sampling hyper‐heuristic for the feature selection problem. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lassouaoui, Mourad – PersonEntity: Name: NameFull: Boughaci, Dalila – PersonEntity: Name: NameFull: Benhamou, Belaid IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2022 Type: published Y: 2022 Identifiers: – Type: issn-print Value: 08247935 Numbering: – Type: volume Value: 38 – Type: issue Value: 3 Titles: – TitleFull: Computational Intelligence Type: main |
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