Ze-HFS: zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorYuan, Kehua
dc.contributor.authorMiao, Duoqian
dc.contributor.authorPedrycz, Witold
dc.contributor.authorDing, Weiping
dc.contributor.authorZhang, Hongyun
dc.date.accessioned2025-04-17T07:00:35Z
dc.date.available2025-04-17T07:00:35Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractKnowledge discovery of heterogeneous data is an active topic in knowledge engineering. Feature selection for heterogeneous data is an important part of effective data analysis. Although there have been many attempts to study the feature selection for heterogeneous data, there are still some challenges, such as the unbalanced problem between the stability and validity of the designed model. Hence, this paper focuses on how to design an effective and robust heterogeneous feature selection method, namely a zentropy-based uncertainty measure for heterogeneous feature selection(Ze-HFS). Different from other entropy-based uncertainty measures, the proposed method does not consider single-level information measures but systematically analyzes and integrates the information between different granular levels, which has an obvious advantage in the study of heterogeneous data knowledge discovery. Specifically, a heterogeneous distance metric is first introduced to construct heterogeneous neighborhood granules and heterogeneous neighborhood rough sets(HNRS). Then, the zentropy-based uncertainty measure is developed by analyzing the granular level structure in the HNRS model. Finally, two significant measures based on the above research are designed for heterogeneous feature selection. Compared with other state-of-the-art methods, the experimental results on 18 public datasets demonstrate the robustness and effectiveness of the proposed method.
dc.description.sponsorshipNational Key Research & Development Program of China
dc.identifier.citationYuan, K., Miao, D., Pedrycz, W., Ding, W., & Zhang, H. (2024). Ze-HFS: Zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery. IEEE Transactions on Knowledge and Data Engineering.
dc.identifier.doi10.1109/TKDE.2024.3419215
dc.identifier.endpage7339
dc.identifier.issn1041-4347
dc.identifier.issn1558-2191
dc.identifier.issue11
dc.identifier.scopus2-s2.0-85197074436
dc.identifier.scopusqualityQ1
dc.identifier.startpage7326
dc.identifier.urihttp://dx.doi.org/10.1109/TKDE.2024.3419215
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6105
dc.identifier.volume36
dc.identifier.wosWOS:001336378400015
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherIEEE computer society
dc.relation.ispartofIEEE transactions on knowledge and data engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectData Mining
dc.subjectFeature Selection
dc.subjectGranular Computing
dc.subjectRough Set
dc.subjectUncertainty Measure
dc.titleZe-HFS: zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery
dc.typeArticle

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