Improving random forest with ensemble of features and semi-supervised feature extraction
- Author
- Junshi Xia, Wenzhi Liao (UGent) , Jocelyn Chanussot, Peijun Du, Guanghan Song and Wilfried Philips (UGent)
- Organization
- Abstract
- In this letter, we propose a novel approach for improving Random Forest (RF) in hyperspectral image classification. The proposed approach combines the ensemble of features and the semisupervised feature extraction (SSFE) technique. The main contribution of our approach is to construct an ensemble of RF classifiers. In this way, the feature space is divided into several disjoint feature subspaces. Then, the feature subspaces induced by the SSFE technique are used as the input space to an RF classifier. This method is compared with a regular RF and an RF with the reduced features by the SSFE on two real hyperspectral data sets, showing an improved performance in ill-posed, poor-posed, and well-posed conditions. An additional study shows that the proposed method is less sensitive to the parameters.
- Keywords
- Ensemble learning, Semi-supervised feature extraction, Classification, Hyperspectral image, Random Forest, SENSING IMAGE CLASSIFICATION, HYPERSPECTRAL DATA, SELECTION, ACCURACY
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-5847840
- MLA
- Xia, Junshi, et al. “Improving Random Forest with Ensemble of Features and Semi-Supervised Feature Extraction.” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, edited by Alejandro Frery, vol. 12, no. 7, IEEE, 2015, pp. 1471–75, doi:10.1109/LGRS.2015.2409112.
- APA
- Xia, J., Liao, W., Chanussot, J., Du, P., Song, G., & Philips, W. (2015). Improving random forest with ensemble of features and semi-supervised feature extraction. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 12(7), 1471–1475. https://doi.org/10.1109/LGRS.2015.2409112
- Chicago author-date
- Xia, Junshi, Wenzhi Liao, Jocelyn Chanussot, Peijun Du, Guanghan Song, and Wilfried Philips. 2015. “Improving Random Forest with Ensemble of Features and Semi-Supervised Feature Extraction.” Edited by Alejandro Frery. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 12 (7): 1471–75. https://doi.org/10.1109/LGRS.2015.2409112.
- Chicago author-date (all authors)
- Xia, Junshi, Wenzhi Liao, Jocelyn Chanussot, Peijun Du, Guanghan Song, and Wilfried Philips. 2015. “Improving Random Forest with Ensemble of Features and Semi-Supervised Feature Extraction.” Ed by. Alejandro Frery. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 12 (7): 1471–1475. doi:10.1109/LGRS.2015.2409112.
- Vancouver
- 1.Xia J, Liao W, Chanussot J, Du P, Song G, Philips W. Improving random forest with ensemble of features and semi-supervised feature extraction. Frery A, editor. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS. 2015;12(7):1471–5.
- IEEE
- [1]J. Xia, W. Liao, J. Chanussot, P. Du, G. Song, and W. Philips, “Improving random forest with ensemble of features and semi-supervised feature extraction,” IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol. 12, no. 7, pp. 1471–1475, 2015.
@article{5847840,
abstract = {{In this letter, we propose a novel approach for improving Random Forest (RF) in hyperspectral image classification. The proposed approach combines the ensemble of features and the semisupervised feature extraction (SSFE) technique. The main contribution of our approach is to construct an ensemble of RF classifiers. In this way, the feature space is divided into several disjoint feature subspaces. Then, the feature subspaces induced by the SSFE technique are used as the input space to an RF classifier. This method is compared with a regular RF and an RF with the reduced features by the SSFE on two real hyperspectral data sets, showing an improved performance in ill-posed, poor-posed, and well-posed conditions. An additional study shows that the proposed method is less sensitive to the parameters.}},
author = {{Xia, Junshi and Liao, Wenzhi and Chanussot, Jocelyn and Du, Peijun and Song, Guanghan and Philips, Wilfried}},
editor = {{Frery, Alejandro}},
issn = {{1545-598X}},
journal = {{IEEE GEOSCIENCE AND REMOTE SENSING LETTERS}},
keywords = {{Ensemble learning,Semi-supervised feature extraction,Classification,Hyperspectral image,Random Forest,SENSING IMAGE CLASSIFICATION,HYPERSPECTRAL DATA,SELECTION,ACCURACY}},
language = {{eng}},
number = {{7}},
pages = {{1471--1475}},
publisher = {{IEEE}},
title = {{Improving random forest with ensemble of features and semi-supervised feature extraction}},
url = {{http://doi.org/10.1109/LGRS.2015.2409112}},
volume = {{12}},
year = {{2015}},
}
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