Supervised incremental feature selection using regularization vector for dynamic multi-scale interval valued datasets.
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| Title: | Supervised incremental feature selection using regularization vector for dynamic multi-scale interval valued datasets. |
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| Authors: | Feng, Zihan1 (AUTHOR) fengzihan1218@163.com, Zhang, Xiaoyan1 (AUTHOR) zxy19790915@163.com |
| Source: | Pattern Recognition. Feb2026, Vol. 170, pN.PAG-N.PAG. 1p. |
| Subjects: | Feature selection, Machine learning, Data mining, Empirical research, Fuzzy decision making, Regularization parameter |
| Abstract: | Feature selection is pivotal for enhancing machine learning and data mining models, where its accuracy directly affects model performance and applicability. Traditional methods often overlook the dynamic nature of data and the multi-scale aspect of high-dimensional datasets, leading to limitations in real-world applications. This paper introduces a novel incremental feature selection method using a regularization vector (R V) tailored for dynamic multi-scale interval valued fuzzy decision systems (D - M I v F D). The paper first establishes the concepts of object affiliation relation and class, providing a theoretical basis for integrating replay and regularization. It then introduces the affiliation contradictory state (A C S) and R V , broadening the application of contradictory state (C S) in dynamic settings and enabling efficient feature selection. The integration of regularization and replay strategies is realized through four algorithms designed for different update patterns. Empirical results across various datasets show that the proposed method significantly outperforms multiple conventional techniques, highlighting its practical potential for real-world deployments. • The paper first establishes the concepts of object affiliation relation and class. • We provide a theoretical basis for integrating replay and regularization. • Regularization and replay strategies is realized within dynamic environments. • Empirical results show that the method significantly outperforms conventional techniques. [ABSTRACT FROM AUTHOR] |
| Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 187839773 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Supervised incremental feature selection using regularization vector for dynamic multi-scale interval valued datasets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Feng%2C+Zihan%22">Feng, Zihan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fengzihan1218@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xiaoyan%22">Zhang, Xiaoyan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zxy19790915@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Pattern+Recognition%22">Pattern Recognition</searchLink>. Feb2026, Vol. 170, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Empirical+research%22">Empirical research</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+decision+making%22">Fuzzy decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Regularization+parameter%22">Regularization parameter</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Feature selection is pivotal for enhancing machine learning and data mining models, where its accuracy directly affects model performance and applicability. Traditional methods often overlook the dynamic nature of data and the multi-scale aspect of high-dimensional datasets, leading to limitations in real-world applications. This paper introduces a novel incremental feature selection method using a regularization vector (R V) tailored for dynamic multi-scale interval valued fuzzy decision systems (D - M I v F D). The paper first establishes the concepts of object affiliation relation and class, providing a theoretical basis for integrating replay and regularization. It then introduces the affiliation contradictory state (A C S) and R V , broadening the application of contradictory state (C S) in dynamic settings and enabling efficient feature selection. The integration of regularization and replay strategies is realized through four algorithms designed for different update patterns. Empirical results across various datasets show that the proposed method significantly outperforms multiple conventional techniques, highlighting its practical potential for real-world deployments. • The paper first establishes the concepts of object affiliation relation and class. • We provide a theoretical basis for integrating replay and regularization. • Regularization and replay strategies is realized within dynamic environments. • Empirical results show that the method significantly outperforms conventional techniques. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science 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: 10.1016/j.patcog.2025.111985 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Data mining Type: general – SubjectFull: Empirical research Type: general – SubjectFull: Fuzzy decision making Type: general – SubjectFull: Regularization parameter Type: general Titles: – TitleFull: Supervised incremental feature selection using regularization vector for dynamic multi-scale interval valued datasets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Feng, Zihan – PersonEntity: Name: NameFull: Zhang, Xiaoyan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00313203 Numbering: – Type: volume Value: 170 Titles: – TitleFull: Pattern Recognition Type: main |
| ResultId | 1 |
