Title :
An incremental extremely random forest classifier for online learning and tracking
Author :
Wang, Aiping ; Wan, Guowei ; Cheng, Zhiquan ; Li, Sikun
Author_Institution :
Sch. of Comput. Sci., Nat. Univ. of Defense Technol., Changsha, China
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.
Keywords :
decision trees; learning (artificial intelligence); object detection; pattern classification; Gini index; UCI datasets; data stream; decision trees; extremely random forest classifier; leaf nodes; online learning classification; video object tracking; video sequences; Classification tree analysis; Computer science; Decision trees; Impurities; Robustness; Semisupervised learning; Streaming media; Support vector machine classification; Support vector machines; Testing; Video tracking; co-training; incremental random forest; online learning; semi-supervised learning;
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
DOI :
10.1109/ICIP.2009.5414559