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http://hdl.handle.net/10397/81646
| Title: | Multi-label bioinformatics data classification with ensemble embedded feature selection | Authors: | Guo, Y Chung, FL Li, GZ Zhang, L |
Issue Date: | 2019 | Source: | IEEE access, 2019, v. 7, p. 103863-103875 | 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. | Keywords: | Bioinformatics Multi-label learning Embedded feature selection |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE access | EISSN: | 2169-3536 | DOI: | 10.1109/ACCESS.2019.2931035 | Rights: | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0 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 |
| Appears in Collections: | Journal/Magazine Article |
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| Guo_Multi-Label_Bioinformatics_Data.pdf | 2.22 MB | Adobe PDF | View/Open |
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