Enhancing minority data generation through optimization in imbalanced datasets: Enhancing minority data generation through optimization...: J. Song et al.
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| Title: | Enhancing minority data generation through optimization in imbalanced datasets: Enhancing minority data generation through optimization...: J. Song et al. |
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| Authors: | Song, Jiuxiang1 (AUTHOR) sjx@email.ncu.edu.cn, Wang, Chuang2 (AUTHOR) wschuangw@163.com, Liu, Jizhong1 (AUTHOR) liujizhong@ncu.edu.cn |
| Source: | Knowledge & Information Systems. May2025, Vol. 67 Issue 5, p4523-4547. 25p. |
| Subjects: | Optimization algorithms, Set functions, Data quality |
| Abstract: | The primary objective of research concerning class imbalance problems revolves around the generation of high-quality data for minority classes. Prior investigations have witnessed various approaches to synthesizing data, resulting in varying data quality. This study introduces a novel oversampling framework, termed the Optimal Oversampling Framework (OOF), which adopts a distinctive perspective. OOF uses optimization algorithms to guide the data generation process, ensuring that new samples are not only similar to the minority class but also exhibit sufficient diversity. Specifically, the method combines initialization and evolutionary strategies to refine the generated samples, while evaluating the similarity of the samples to the minority and majority classes through distance and cosine similarity measures. In addition, OOF prevents premature convergence and ensures that the generated samples maintain uniqueness through diversity judgments and fitness function settings. Finally, OOF selects the best quality samples for oversampling by optimizing the ranking. To demonstrate the effectiveness of OOF, we integrated the Particle Swarm Optimization algorithm with OOF and conducted comparative experiments involving nine different oversampling methods across 21 datasets characterized by high class imbalance ratios. The experimental outcomes validate the success of the OOF approach. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge & Information Systems is the property of Springer Nature 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: 184453194 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing minority data generation through optimization in imbalanced datasets: Enhancing minority data generation through optimization...: J. Song et al. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Song%2C+Jiuxiang%22">Song, Jiuxiang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sjx@email.ncu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Chuang%22">Wang, Chuang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> wschuangw@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Jizhong%22">Liu, Jizhong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> liujizhong@ncu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Knowledge+%26+Information+Systems%22">Knowledge & Information Systems</searchLink>. May2025, Vol. 67 Issue 5, p4523-4547. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Set+functions%22">Set functions</searchLink><br /><searchLink fieldCode="DE" term="%22Data+quality%22">Data quality</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The primary objective of research concerning class imbalance problems revolves around the generation of high-quality data for minority classes. Prior investigations have witnessed various approaches to synthesizing data, resulting in varying data quality. This study introduces a novel oversampling framework, termed the Optimal Oversampling Framework (OOF), which adopts a distinctive perspective. OOF uses optimization algorithms to guide the data generation process, ensuring that new samples are not only similar to the minority class but also exhibit sufficient diversity. Specifically, the method combines initialization and evolutionary strategies to refine the generated samples, while evaluating the similarity of the samples to the minority and majority classes through distance and cosine similarity measures. In addition, OOF prevents premature convergence and ensures that the generated samples maintain uniqueness through diversity judgments and fitness function settings. Finally, OOF selects the best quality samples for oversampling by optimizing the ranking. To demonstrate the effectiveness of OOF, we integrated the Particle Swarm Optimization algorithm with OOF and conducted comparative experiments involving nine different oversampling methods across 21 datasets characterized by high class imbalance ratios. The experimental outcomes validate the success of the OOF approach. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Knowledge & Information Systems is the property of Springer Nature 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: 10.1007/s10115-025-02361-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 4523 Subjects: – SubjectFull: Optimization algorithms Type: general – SubjectFull: Set functions Type: general – SubjectFull: Data quality Type: general Titles: – TitleFull: Enhancing minority data generation through optimization in imbalanced datasets: Enhancing minority data generation through optimization...: J. Song et al. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Song, Jiuxiang – PersonEntity: Name: NameFull: Wang, Chuang – PersonEntity: Name: NameFull: Liu, Jizhong IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02191377 Numbering: – Type: volume Value: 67 – Type: issue Value: 5 Titles: – TitleFull: Knowledge & Information Systems Type: main |
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