Feature Selection Using Neighborhood Positive Region Certainty.
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| Title: | Feature Selection Using Neighborhood Positive Region Certainty. |
|---|---|
| Authors: | Lu, Zhengcai1,2 (AUTHOR) zhengcai.lu@lzy.edu.cn, Tian, Zhengwei1,2 (AUTHOR), Wong, Man Leung (AUTHOR) mohabib@wiley.com |
| Source: | Journal of Applied Mathematics. 3/30/2026, Vol. 2026, p1-14. 14p. |
| Subjects: | Feature selection, Rough sets, Machine learning, Pattern recognition systems, Classification |
| Abstract: | Neighborhood rough set–based attribute reduction is a powerful tool used widely in areas such as machine learning, pattern recognition, and decision support to handle numerical data. Before performing a classification task, it is necessary to find a subset of features that possesses the same classification ability as the entire feature set. To address this requirement, numerous neighborhood rough set–based attribute reduction algorithms have been developed and applied to numerical data. These algorithms almost exclusively utilize positive region information to assess the classification ability of attributes, with minimal reliance on boundary region information. This study proposes a new efficient reduction algorithm using neighborhood positive region certainty (NPRC). It fully leverages both positive region and boundary region information, leading to a significant enhancement of algorithm performance. Firstly, we introduce a novel technique termed neighborhood partition, aiming to gain a deeper understanding of neighborhoods and reveal valuable knowledge. Subsequently, we develop a new model called the partitioned neighborhood rough set, which revolutionizes the rules for determining the region to which an object belongs. Furthermore, we put forward an attribute evaluation method, referred to as NPRC. It not only considers positive region objects but also takes into account the contribution of the boundary region objects to the positive region, extending its value from 0 or 1 to a continuous value between 0 and 1. This innovation provides a more concrete and comprehensive description of the classification ability of attributes. Finally, we design a new attribute reduction algorithm that utilizes NPRC to evaluate attributes and guide a greedy search process to find an optimal subset of features. Experimental results demonstrate that the proposed algorithm is capable of discovering a smaller number of attributes and achieves better classification performance compared to other available algorithms. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Mathematics 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: 192628999 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Feature Selection Using Neighborhood Positive Region Certainty. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lu%2C+Zhengcai%22">Lu, Zhengcai</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> zhengcai.lu@lzy.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tian%2C+Zhengwei%22">Tian, Zhengwei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wong%2C+Man+Leung%22">Wong, Man Leung</searchLink> (AUTHOR)<i> mohabib@wiley.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Mathematics%22">Journal of Applied Mathematics</searchLink>. 3/30/2026, Vol. 2026, p1-14. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Rough+sets%22">Rough sets</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Classification%22">Classification</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Neighborhood rough set–based attribute reduction is a powerful tool used widely in areas such as machine learning, pattern recognition, and decision support to handle numerical data. Before performing a classification task, it is necessary to find a subset of features that possesses the same classification ability as the entire feature set. To address this requirement, numerous neighborhood rough set–based attribute reduction algorithms have been developed and applied to numerical data. These algorithms almost exclusively utilize positive region information to assess the classification ability of attributes, with minimal reliance on boundary region information. This study proposes a new efficient reduction algorithm using neighborhood positive region certainty (NPRC). It fully leverages both positive region and boundary region information, leading to a significant enhancement of algorithm performance. Firstly, we introduce a novel technique termed neighborhood partition, aiming to gain a deeper understanding of neighborhoods and reveal valuable knowledge. Subsequently, we develop a new model called the partitioned neighborhood rough set, which revolutionizes the rules for determining the region to which an object belongs. Furthermore, we put forward an attribute evaluation method, referred to as NPRC. It not only considers positive region objects but also takes into account the contribution of the boundary region objects to the positive region, extending its value from 0 or 1 to a continuous value between 0 and 1. This innovation provides a more concrete and comprehensive description of the classification ability of attributes. Finally, we design a new attribute reduction algorithm that utilizes NPRC to evaluate attributes and guide a greedy search process to find an optimal subset of features. Experimental results demonstrate that the proposed algorithm is capable of discovering a smaller number of attributes and achieves better classification performance compared to other available algorithms. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Mathematics 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: 10.1155/jama/8274166 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 1 Subjects: – SubjectFull: Feature selection Type: general – SubjectFull: Rough sets Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Classification Type: general Titles: – TitleFull: Feature Selection Using Neighborhood Positive Region Certainty. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lu, Zhengcai – PersonEntity: Name: NameFull: Tian, Zhengwei – PersonEntity: Name: NameFull: Wong, Man Leung IsPartOfRelationships: – BibEntity: Dates: – D: 30 M: 03 Text: 3/30/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1110757X Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Applied Mathematics Type: main |
| ResultId | 1 |
