Deep Fuzzy Envelope Sample Generation Mechanism for Imbalanced Ensemble Classification

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dc.contributor.authorLi, Fan
dc.contributor.authorLi, Yongming
dc.contributor.authorShen, Yinghua
dc.contributor.authorPedrycz, Witold
dc.contributor.authorZhang, Xiaoheng
dc.contributor.authorWang, Pin
dc.contributor.authorLi, Pufei
dc.date.accessioned2024-05-19T14:41:17Z
dc.date.available2024-05-19T14:41:17Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractEnsemble methods are widely used to tackle class imbalance problem. However, for existing imbalanced ensemble (IE) methods, the samples in each subset are resampled from the same dataset, and are directly input to the classifier for training, so the quality (diversity and separability) of the subsets is unsatisfactory usually. To solve the problem, a deep fuzzy envelope sample generation mechanism is proposed. First, the fuzzy C-means clustering based deep sample envelope prenetwork (DSEN) is designed to mine correlation information among samples, thereby increasing the quality of the subsets. Second, the local manifold structure metric and global structure distribution metric are designed to construct local-global structure consistency mechanism (LGSCM) to enhance distribution consistency of interlayer samples of DSEN. Third, the DSEN and LGSCM are combined to form the final deep sample envelope network-DSENLG to refresh the existing subsets. Finally, base classifiers are applied on the new subsets generated by the DSENLG and then fused, thereby realizing a new IE algorithm. The experimental results show that the proposed algorithm is significantly better than existing representative IE algorithms and it achieves the highest improvement of 10.64%, 19.5%, 18.67% and 22.33% on four criteria over the state-of-the-art methods. The originality of the article is threefold: proposing the concept of deep fuzzy samples or envelope samples, which comprehensively considers the correlation information among original samples; proposing the LGSCM to resolve the distribution inconsistency of interlayer samples; and forming an fuzzy envelope sample based IE algorithm.en_US
dc.description.sponsorshipNational Natural Science Foundation of Chinaen_US
dc.description.sponsorshipNo Statement Availableen_US
dc.identifier.doi10.1109/TFUZZ.2023.3321768
dc.identifier.endpage1262en_US
dc.identifier.issn1063-6706
dc.identifier.issn1941-0034
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85174831116en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1248en_US
dc.identifier.urihttps://doi.org10.1109/TFUZZ.2023.3321768
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5088
dc.identifier.volume32en_US
dc.identifier.wosWOS:001179721500013en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Transactions on Fuzzy Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectEnsemble Learningen_US
dc.subjectCorrelationen_US
dc.subjectClassification Algorithmsen_US
dc.subjectTrainingen_US
dc.subjectManifoldsen_US
dc.subjectMeasurementen_US
dc.subjectClustering Algorithmsen_US
dc.subjectClass Imbalance Problemen_US
dc.subjectDomain Adaptionen_US
dc.subjectEnsemble Approachen_US
dc.subjectEnvelope Learningen_US
dc.subjectFuzzy C-Means (Fcms) Clusteringen_US
dc.titleDeep Fuzzy Envelope Sample Generation Mechanism for Imbalanced Ensemble Classificationen_US
dc.typeArticleen_US

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