Please use this identifier to cite or link to this item: 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
Keywords: Bioinformatics
Multi-label learning
Embedded feature selection
Issue Date: 2019
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2019, v. 7, p. 103863-103875 How to cite?
Journal: IEEE access 
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.
URI: http://hdl.handle.net/10397/81646
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

Files in This Item:
File Description SizeFormat 
Guo_Multi-Label_Bioinformatics_Data.pdf2.22 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

20
Citations as of May 6, 2020

Download(s)

14
Citations as of May 6, 2020

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.