An incremental extremely random forest classifier for online learning and tracking

Authors:
Aiping Wang;Guowei Wan;Zhiquan Cheng;Sikun Li
Affiliations:
School of Computer Science, National University of Defense Technology, Changsha, Hunan Province, China;School of Computer Science, National University of Defense Technology, Changsha, Hunan Province, China;School of Computer Science, National University of Defense Technology, Changsha, Hunan Province, China;School of Computer Science, National University of Defense Technology, Changsha, Hunan Province, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Decision trees have been widely used for online learning classification. Many approaches usually need large data stream to finish decision trees induction, as show notable limitations (even fail) with small data stream. In fact, there exist many real instances with small data stream. In the paper, we propose a novel incremental extremely random forest algorithm, dealing with online learning classification with small streaming data. In our method, arriving examples are stored at the leaf nodes and used to determine when to split the leaf nodes combined with Gini index, so the trees can be expanded efficiently with a few examples. Our algorithm has been applied to solve both online learning and video object tracking problems, and the results on UCI datasets and challenging video sequences demonstrate its effectiveness and robustness.