An Ensemble Method for High-Dimensional Multilabel Data
Joint Authors
Liu, Huawen
Zheng, Zhonglong
Zhao, Jianmin
Ye, Ronghua
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-5, 5 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-11-26
Country of Publication
Egypt
No. of Pages
5
Main Subjects
Abstract EN
Multilabel learning is now receiving an increasing attention from a variety of domains and many learning algorithms have been witnessed.
Similarly, the multilabel learning may also suffer from the problems of high dimensionality, and little attention has been paid to this issue.
In this paper, we propose a new ensemble learning algorithms for multilabel data.
The main characteristic of our method is that it exploits the features with local discriminative capabilities for each label to serve the purpose of classification.
Specifically, for each label, the discriminative capabilities of features on positive and negative data are estimated, and then the top features with the highest capabilities are obtained.
Finally, a binary classifier for each label is constructed on the top features.
Experimental results on the benchmark data sets show that the proposed method outperforms four popular and previously published multilabel learning algorithms.
American Psychological Association (APA)
Liu, Huawen& Zheng, Zhonglong& Zhao, Jianmin& Ye, Ronghua. 2013. An Ensemble Method for High-Dimensional Multilabel Data. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-5.
https://search.emarefa.net/detail/BIM-1008700
Modern Language Association (MLA)
Liu, Huawen…[et al.]. An Ensemble Method for High-Dimensional Multilabel Data. Mathematical Problems in Engineering No. 2013 (2013), pp.1-5.
https://search.emarefa.net/detail/BIM-1008700
American Medical Association (AMA)
Liu, Huawen& Zheng, Zhonglong& Zhao, Jianmin& Ye, Ronghua. An Ensemble Method for High-Dimensional Multilabel Data. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-5.
https://search.emarefa.net/detail/BIM-1008700
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1008700