Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81646
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dc.contributorDepartment of Computing-
dc.creatorGuo, Y-
dc.creatorChung, FL-
dc.creatorLi, GZ-
dc.creatorZhang, L-
dc.date.accessioned2020-02-10T12:28:24Z-
dc.date.available2020-02-10T12:28:24Z-
dc.identifier.urihttp://hdl.handle.net/10397/81646-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsThis work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0en_US
dc.rightsThe following publication Y. Guo, F. Chung, G. Li and L. Zhang, "Multi-Label Bioinformatics Data Classification With Ensemble Embedded Feature Selection," in IEEE Access, vol. 7, pp. 103863-103875, 2019 is available at https://dx.doi.org/10.1109/ACCESS.2019.2931035en_US
dc.subjectBioinformaticsen_US
dc.subjectMulti-label learningen_US
dc.subjectEmbedded feature selectionen_US
dc.titleMulti-label bioinformatics data classification with ensemble embedded feature selectionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage103863-
dc.identifier.epage103875-
dc.identifier.volume7-
dc.identifier.doi10.1109/ACCESS.2019.2931035-
dcterms.abstractIn bioinformatics, the vast of multi-label type of datasets, including clinical text, gene, and protein data, need to be categorized. Specifically, due to the redundant or irrelevant features in bioinformatics data, the performance of multi-label classifiers will be limited, and therefore, selecting effective features from the feature space is necessary. However, most of the proposed methods, which aimed at dealing with multi-label feature selection problem in the past few years, only adopt a simple and direct strategy that transforms the multi-label feature selection problem into more single-label ones and ignore correlations among different labels. In this paper, a novel algorithm named ensemble embedded feature selection (EEFS) is proposed to handle multi-label bioinformatics data learning problem in a more effective and efficient way. The EEFS does not only explicitly find out the correlations among labels, but it can also adequately utilize the label correlations by multi-label classifiers and evaluation measures. Furthermore, it can reduce the accumulated errors of data itself by employing an ensemble method. The experimental results on five multi-label bioinformatics datasets show that our algorithm achieves significant superiority over the other state-of-the-art algorithms.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE access, 2019, v. 7, p. 103863-103875-
dcterms.isPartOfIEEE access-
dcterms.issued2019-
dc.identifier.isiWOS:000481692400019-
dc.identifier.eissn2169-3536-
dc.description.validate202002 bcrc-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_Scopus/WOSen_US
dc.description.pubStatusPublisheden_US
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