機械知覚&ロボティクスグループ
中部大学

Machine Learning 国際会議

Boosted Random Forest

Author
Yohei Mishina, Masamitsu Tsuchiya, Hironobu Fujiyoshi
Publication
International Joint Conference on Computer Vision,Imaging and Computer Graphics Theory and Applications, Vol.2, pp.594-598, 2014

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The ability of generalization by random forests is higher than that by other multi-class classifiers because of the effect of bagging and feature selection. Since random forests based on ensemble learning requires a lot of decision trees to obtain high performance, it is not suitable for implementing the algorithm on the small-scale hardware such as embedded system. In this paper, we propose a boosted random forests in which boosting algorithm is introduced into random forests. Experimental results show that the proposed method, which consists of fewer decision trees, has higher generalization ability comparing to the conventional method.

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