Please use this identifier to cite or link to this item:
Title: Progressive subspace ensemble learning
Authors: Yu, Z
Wang, D
You, J 
Wong, HS
Wu, S
Zhang, J
Han, G
Keywords: AdaBoost
Classifier ensemble
Decision tree
Ensemble learning
Random subspace
Issue Date: 2016
Publisher: Elsevier
Source: Pattern recognition, 2016, v. 60, p. 692-705 How to cite?
Journal: Pattern recognition 
Abstract: There are not many classifier ensemble approaches which investigate the data sample space and the feature space at the same time, and this multi-pronged approach will be helpful for constructing more powerful learning models. For example, the AdaBoost approach only investigates the data sample space, while the random subspace technique only focuses on the feature space. To address this limitation, we propose the progressive subspace ensemble learning approach (PSEL) which takes into account the data sample space and the feature space at the same time. Specifically, PSEL first adopts the random subspace technique to generate a set of subspaces. Then, a progressive selection process based on new cost functions that incorporate current and long-term information to select the classifiers sequentially will be introduced. Finally, a weighted voting scheme is used to summarize the predicted labels and obtain the final result. We also adopt a number of non-parametric tests to compare PSEL and its competitors over multiple datasets. The results of the experiments show that PSEL works well on most of the real datasets, and outperforms a number of state-of-the-art classifier ensemble approaches.
ISSN: 0031-3203
EISSN: 1873-5142
DOI: 10.1016/j.patcog.2016.06.017
Appears in Collections:Journal/Magazine Article

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Checked on Aug 13, 2017

Google ScholarTM



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