An Ensemble Method for High-Dimensional Multilabel Data
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 pr...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2013-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2013/208051 |
id |
doaj-59a7ef0ea19541f6806400c66e6a859b |
---|---|
record_format |
Article |
spelling |
doaj-59a7ef0ea19541f6806400c66e6a859b2020-11-24T20:50:58ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/208051208051An Ensemble Method for High-Dimensional Multilabel DataHuawen Liu0Zhonglong Zheng1Jianmin Zhao2Ronghua Ye3College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua 321004, ChinaMultilabel 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.http://dx.doi.org/10.1155/2013/208051 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huawen Liu Zhonglong Zheng Jianmin Zhao Ronghua Ye |
spellingShingle |
Huawen Liu Zhonglong Zheng Jianmin Zhao Ronghua Ye An Ensemble Method for High-Dimensional Multilabel Data Mathematical Problems in Engineering |
author_facet |
Huawen Liu Zhonglong Zheng Jianmin Zhao Ronghua Ye |
author_sort |
Huawen Liu |
title |
An Ensemble Method for High-Dimensional Multilabel Data |
title_short |
An Ensemble Method for High-Dimensional Multilabel Data |
title_full |
An Ensemble Method for High-Dimensional Multilabel Data |
title_fullStr |
An Ensemble Method for High-Dimensional Multilabel Data |
title_full_unstemmed |
An Ensemble Method for High-Dimensional Multilabel Data |
title_sort |
ensemble method for high-dimensional multilabel data |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/2013/208051 |
work_keys_str_mv |
AT huawenliu anensemblemethodforhighdimensionalmultilabeldata AT zhonglongzheng anensemblemethodforhighdimensionalmultilabeldata AT jianminzhao anensemblemethodforhighdimensionalmultilabeldata AT ronghuaye anensemblemethodforhighdimensionalmultilabeldata AT huawenliu ensemblemethodforhighdimensionalmultilabeldata AT zhonglongzheng ensemblemethodforhighdimensionalmultilabeldata AT jianminzhao ensemblemethodforhighdimensionalmultilabeldata AT ronghuaye ensemblemethodforhighdimensionalmultilabeldata |
_version_ |
1716803110669647872 |