Fine-grained local label correlation for multi-label classification

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorZhao, Tianna
dc.contributor.authorZhang, Yuanjian
dc.contributor.authorMiao, Duoqian
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2025-04-17T08:20:03Z
dc.date.available2025-04-17T08:20:03Z
dc.date.issued2025
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractComprehensive learning label correlation is conducive to boosting the accuracy of multi-label classification. While existing methods focus on exploring the correlation-aware original features or latent subspaces, they often overlook the role of correlation in deducing local structures. The oversight can result in suboptimal topic-based label correlation estimation and thus incur information loss. In contrast to the conventional single- granularity-based learning for local label correlation, we propose a multi-granularity correlation-based feature augmentation (MGOFA) model. MGOFA consists of three components that progressively refine the granularity of label correlation: granular-based feature augmentation for relative neighborhood-based class tendency estimation, granular-based latent topic mining for tendency-aware topic modeling, and fine-grained label correlation mining for augmented local label correlation learning. The information on neighborhood-based similarity between instances is explicitly leveraged and contributes to the model two-fold. Firstly, it induces the prototypes of latent topics, which share more knowledge with the label association. Secondly, it refines the discriminative granularity of the model by integrating it with the original features. Such a formulation simulates the viewpoint of human decision-making by automatically determining optimal solutions on both data and knowledge from coarse and refined granularity, respectively. Extensive comparisons completed often benchmarks demonstrate that MGOFA outperforms the state-of-the-art methods with satisfying convergence and sensitivity.
dc.description.sponsorshipNational Key Research and Development Program of China National Natural Science Foundation of China
dc.identifier.citationZhao, T., Zhang, Y., Miao, D., & Pedrycz, W. (2025). Fine-grained local label correlation for multi-label classification. Knowledge-Based Systems, 113210.
dc.identifier.doi10.1016/j.knosys.2025.113210
dc.identifier.endpage20
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85219133504
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.knosys.2025.113210
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6158
dc.identifier.volume314
dc.identifier.wosWOS:001439635000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherElsevier b.v.
dc.relation.ispartofKnowledge-based systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectFeature Augmentation
dc.subjectLabel-Specific Features
dc.subjectLocal Label Correlation
dc.subjectMulti-Granularity
dc.subjectMulti-Label Classification
dc.titleFine-grained local label correlation for multi-label classification
dc.typeArticle

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