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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
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
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