Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82231
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dc.contributorDepartment of Computing-
dc.creatorXu, RFen_US
dc.creatorWen, ZYen_US
dc.creatorGui, Len_US
dc.creatorLu, Qen_US
dc.creatorLi, BYen_US
dc.creatorWang, XZen_US
dc.date.accessioned2020-05-05T05:59:12Z-
dc.date.available2020-05-05T05:59:12Z-
dc.identifier.issn1868-8071en_US
dc.identifier.urihttp://hdl.handle.net/10397/82231-
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.rights© The Author(s) 2019en_US
dc.rightsOpen Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en_US
dc.rightsThe following publication Xu, R., Wen, Z., Gui, L. et al. Ensemble with estimation: seeking for optimization in class noisy data. Int. J. Mach. Learn. & Cyber. 11, 231–248 (2020) is available at https://dx.doi.org/10.1007/s13042-019-00969-8en_US
dc.subjectClass noiseen_US
dc.subjectEnsemble learningen_US
dc.subjectMachine learningen_US
dc.titleEnsemble with estimation : seeking for optimization in class noisy dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage231en_US
dc.identifier.epage248en_US
dc.identifier.volume11en_US
dc.identifier.issue2en_US
dc.identifier.doi10.1007/s13042-019-00969-8en_US
dcterms.abstractClass noise, as know as the mislabeled data in training set, can lead to poor accuracy in classification no matter what machine learning methods are used. A reasonable estimation of class noise has a significant impact on the performance of learning methods. However, the error in existing estimation is inevitable theoretically and infer the performance of optimal classifier trained on noisy data. Instead of seeking a single optimal classifier on noisy data, in this work, we use a set of weak classifiers, which are caused by negative impacts of noisy data, to learn an ensemble strong classifier which is based on the training error and estimation of class noise. By this strategy, the proposed ensemble with estimation method overcomes the gap between the estimation and true distribution of class noise. Our proposed method does not require any a priori knowledge about class noises. We prove that the optimal ensemble classifier on the noisy distribution can approximate the optimal classifier on the clean distribution when the training set grows. Comparisons with existing algorithms show that our methods outperform state-of-the-art approaches on a large number of benchmark datasets in different domains. Both the theoretical analysis and the experimental result reveal that our method can improve the performance, works well on clean data and is robust on the algorithm parameter.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationInternational journal of machine learning and cybernetics, 12 June 2020, v. 11, no. 2, p. 231-248en_US
dcterms.isPartOfInternational journal of machine learning and cyberneticsen_US
dcterms.issued2020-
dc.identifier.isiWOS:000512019400001-
dc.identifier.scopus2-s2.0-85067656495-
dc.identifier.eissn1868-808Xen_US
dc.description.validate202006 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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