IEICE TRANSACTIONS on Information
Specific Random Trees for Random Forest
Summary :
In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E96-D No.3 pp.739-741
- Publication Date
- 2013/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E96.D.739
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Zhi LIU, Zhaocai SUN, Hongjun WANG, "Specific Random Trees for Random Forest" in IEICE TRANSACTIONS on Information,
vol. E96-D, no. 3, pp. 739-741, March 2013, doi: 10.1587/transinf.E96.D.739.
Abstract: In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E96.D.739/_p
Copy
@ARTICLE{e96-d_3_739,
author={Zhi LIU, Zhaocai SUN, Hongjun WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Specific Random Trees for Random Forest},
year={2013},
volume={E96-D},
number={3},
pages={739-741},
abstract={In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.},
keywords={},
doi={10.1587/transinf.E96.D.739},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - Specific Random Trees for Random Forest
T2 - IEICE TRANSACTIONS on Information
SP - 739
EP - 741
AU - Zhi LIU
AU - Zhaocai SUN
AU - Hongjun WANG
PY - 2013
DO - 10.1587/transinf.E96.D.739
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E96-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2013
AB - In this study, a novel forest method based on specific random trees (SRT) was proposed for a multiclass classification problem. The proposed SRT was built on one specific class, which decides whether a sample belongs to a certain class. The forest can make a final decision on classification by ensembling all the specific trees. Compared with the original random forest, our method has higher strength, but lower correlation and upper error bound. The experimental results based on 10 different public datasets demonstrated the efficiency of the proposed method.
ER -
English
