Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/81646
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Guo, Y | - |
dc.creator | Chung, FL | - |
dc.creator | Li, GZ | - |
dc.creator | Zhang, L | - |
dc.date.accessioned | 2020-02-10T12:28:24Z | - |
dc.date.available | 2020-02-10T12:28:24Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/81646 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | The 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.2019.2931035 | en_US |
dc.subject | Bioinformatics | en_US |
dc.subject | Multi-label learning | en_US |
dc.subject | Embedded feature selection | en_US |
dc.title | Multi-label bioinformatics data classification with ensemble embedded feature selection | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 103863 | - |
dc.identifier.epage | 103875 | - |
dc.identifier.volume | 7 | - |
dc.identifier.doi | 10.1109/ACCESS.2019.2931035 | - |
dcterms.abstract | In 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE access, 2019, v. 7, p. 103863-103875 | - |
dcterms.isPartOf | IEEE access | - |
dcterms.issued | 2019 | - |
dc.identifier.isi | WOS:000481692400019 | - |
dc.identifier.eissn | 2169-3536 | - |
dc.description.validate | 202002 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Guo_Multi-Label_Bioinformatics_Data.pdf | 2.22 MB | Adobe PDF | View/Open |
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