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Proceedings Paper

Random forest using tree selection method to classify unbalanced data
Author(s): Baoxun Xu; Yunming Ye; Qiang Wang; Junjie Li; Xiaojun Chen
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Paper Abstract

Random forest is a popular classification algorithm used to build ensemble models of decision tree classifiers. However, owing to the complexity of unbalanced data distribution in high dimensional space, a random forest may include bad trees that can result in wrong results. This paper proposed an improved random forest algorithm with tree selection methods. This algorithm is particularly designed for analyzing unbalanced data. The novel tree selection methods are developed for making random forest framework well suited to classify unbalanced data. Experimental results on unbalanced datasets with diverse characteristics have demonstrated that the proposed method could generate a random forest model with higher performance than the random forests generated by Breiman's method.

Paper Details

Date Published: 8 June 2012
PDF: 6 pages
Proc. SPIE 8334, Fourth International Conference on Digital Image Processing (ICDIP 2012), 83344F (8 June 2012); doi: 10.1117/12.970545
Show Author Affiliations
Baoxun Xu, Harbin Institute of Technology (China)
Yunming Ye, Harbin Institute of Technology (China)
Qiang Wang, Harbin Institute of Technology (China)
Junjie Li, Shenzhen Institutes of Advanced Technology (China)
Xiaojun Chen, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 8334:
Fourth International Conference on Digital Image Processing (ICDIP 2012)
Mohamed Othman; Sukumar Senthilkumar; Xie Yi, Editor(s)

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