Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61068
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorDong, A-
dc.creatorChung, FL-
dc.creatorWang, S-
dc.date.accessioned2016-12-19T08:54:36Z-
dc.date.available2016-12-19T08:54:36Z-
dc.identifier.issn0020-0255-
dc.identifier.urihttp://hdl.handle.net/10397/61068-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectCommon hidden spaceen_US
dc.subjectDimensionality augmentationen_US
dc.subjectOversamplingen_US
dc.subjectSemi-supervised classificationen_US
dc.titleSemi-supervised classification method through oversampling and common hidden spaceen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage216-
dc.identifier.epage228-
dc.identifier.volume349-350-
dc.identifier.doi10.1016/j.ins.2016.02.042-
dcterms.abstractSemi-supervised classification methods attempt to improve classification performance based on a small amount of labeled data through full use of abundant unlabeled data. Although existing semi-supervised classification methods have exhibited promising results in many applications, they still have drawbacks, including performance degeneration, due to the introduction of unlabeled data and partially false labels in a small amount of labeled data. To circumvent such drawbacks, a new semi-supervised classification method OCHS-SSC through oversampling and a common hidden space is proposed in the paper. The primary characteristics of the proposed method include two aspects. One is that unlabeled data are only used to generate new synthetic data to extend the minimal amount of labeled data. The other is that the final classifier is learned in the extended feature space, which is composed of the original feature space and the common hidden space found between labeled data and the synthetic data instead of the original feature space. Extensive experiments on 23 datasets indicate the effectiveness of the proposed method.-
dcterms.bibliographicCitationInformation sciences, 2016, v. 349-350, p. 216-228-
dcterms.isPartOfInformation sciences-
dcterms.issued2016-
dc.identifier.isiWOS:000374081300014-
dc.identifier.scopus2-s2.0-84960449255-
dc.identifier.ros2016003933-
dc.identifier.eissn1872-6291-
dc.identifier.rosgroupid2016003862-
dc.description.ros2016-2017 > Academic research: refereed > Publication in refereed journal-
dc.description.validate201804_a bcma-
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