Multigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection

Basit Öğe Kaydı
dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorYuan, Kehua
dc.contributor.authorMiao, Duoqian
dc.contributor.authorPedrycz, Witold
dc.contributor.authorZhang, Hongyun
dc.contributor.authorHu, Liang
dc.date.accessioned2025-04-18T10:09:36Z
dc.date.available2025-04-18T10:09:36Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractMultigranularity data analysis has recently become an active research topic in the intelligent computing and data mining fields. Feature selection via multigranularity data analysis is an effective tool for characterizing hierarchical data and enhancing the accuracy of the results. Although the multigranularity data analysis method has been widely adopted for feature selection, existing studies still present one prevalent disadvantage: multigranularity data analysis mostly focuses on information presented at a single granularity while ignoring the hierarchical structure of multigranularity data, which is contrary to the nature of multigranularity. Hence, this article proposes a multigranularity data analysis with a zentropy uncertainty measure for efficient and robust feature selection. Specifically, a consistent degree is first introduced to obtain optimal granularity combinations and establish an efficient neighborhood model for multigranularity information processing. Then, a novel and robust uncertainty measure is developed by integrating the multigranularity information, namely the zentropy-based measure. Considering its accuracy among uncertainty measures, two important measures are further designed and applied to feature selection. Extensive experiments demonstrate that the proposed method can achieve better robustness and classification performance than other state-of-the-art methods. © 2013 IEEE.
dc.description.sponsorshipThis work was supported in part by the National Key Research and Development Program of China \"Key Special Project on Cyberspace Security Governance\" under Grant 2022YFB3104700, and in part by the National Natural Science Foundation of China under Grant 61976158 and Grant 62376198. This article was recommended by Associate Editor C.- Y. Su.
dc.identifier.citationYuan, K., Miao, D., Pedrycz, W., Zhang, H., & Hu, L. (2024). Multigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection. IEEE Transactions on Cybernetics.
dc.identifier.doi10.1109/TCYB.2024.3499952
dc.identifier.endpage752
dc.identifier.issn21682267
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85211498407
dc.identifier.scopusqualityQ1
dc.identifier.startpage740
dc.identifier.urihttp://dx.doi.org/10.1109/TCYB.2024.3499952
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6967
dc.identifier.volume55
dc.identifier.wosWOS:001371975700001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Cybernetics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFeature Selection
dc.subjectGranular Computing
dc.subjectMultigranularity Data Analysis
dc.subjectRough Set
dc.subjectUncertainty Measure
dc.titleMultigranularity Data Analysis With Zentropy Uncertainty Measure for Efficient and Robust Feature Selection
dc.typeArticle

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bilgisayar Mühendisliği Bölümü Makale Koleksiyonu