A Robust three-way classifier with shadowed granular balls based on justifiable granularity

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorYang, Jie
dc.contributor.authorXiaodiao, Lingyun
dc.contributor.authorWang, Guoyin
dc.contributor.authorPedrycz, Witold
dc.contributor.authorXia, Shuyin
dc.contributor.authorZhang, Qinghua
dc.contributor.authorWu, Di
dc.date.accessioned2025-06-18T13:21:49Z
dc.date.available2025-06-18T13:21:49Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractThe granular-ball (GB)-based classifier introduced by Xia exhibits adaptability in creating coarse-grained information granules for input, thereby enhancing its generality and flexibility. Nevertheless, the current GB-based classifiers rigidly assign a specific class label to each data instance and lack the necessary strategies to address uncertain instances. These far-fetched certain classification approaches toward uncertain instances may suffer considerable risks. To solve this problem, we construct a robust three-way classifier with shadowed GBs (3WC-SGBs) for uncertain data. First, combined with information entropy, we propose an enhanced GB generation method with the principle of justifiable granularity. Subsequently, based on minimum uncertainty, a shadowed mapping is utilized to partition a GB into core region (COR), important region (IMP), and unessential region (UNE). Based on the constructed shadowed GBs, we establish a three-way classifier to categorize data instances into certain classes and uncertain case. Finally, extensive comparative experiments are conducted with two three-way classifiers, three state-of-the-art GB-based classifiers, and three classical machine learning classifiers on 12 public benchmark datasets. The results show that our model demonstrates robustness in managing uncertain data and effectively mitigates classification risks. Furthermore, our model almost outperforms the other comparison methods in both effectiveness and efficiency.
dc.identifier.citationYang, J., Xiaodiao, L., Wang, G., Pedrycz, W., Xia, S., Zhang, Q., & Wu, D. (2025). A Robust Three-Way Classifier With Shadowed Granular Balls Based on Justifiable Granularity. IEEE Transactions on Neural Networks and Learning Systems.
dc.identifier.doi10.1109/TNNLS.2025.3563889
dc.identifier.endpage15
dc.identifier.issn2162-237X
dc.identifier.issn2162-2388
dc.identifier.pmid40388284
dc.identifier.scopus2-s2.0-105005773403
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1109/TNNLS.2025.3563889
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7316
dc.identifier.wosWOS:001494226800001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
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 neural networks and learning systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectGranular-Ball (GB) Generation
dc.subjectJustifiable Granularity
dc.subjectShadowed GBs
dc.subjectThree-Way Classifier
dc.titleA Robust three-way classifier with shadowed granular balls based on justifiable granularity
dc.typeArticle

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