Boosted Random Forest

Yohei Mishina; Masamitsu Tsuchiya; Hironobu Fujiyoshi

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Paper

Boosted Random Forest

Topics: Categorization and Scene Understanding; Object and Face Recognition

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, 594-598, 2014 , Lisbon, Portugal

Authors: Yohei Mishina ; Masamitsu Tsuchiya and Hironobu Fujiyoshi

Affiliation: Chubu University, Japan

Keyword(s): Boosting, Random Forest, Machine Learning, Pattern Recognition.

Abstract: 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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Paper citation in several formats:

Mishina, Y.; Tsuchiya, M. and Fujiyoshi, H. (2014). Boosted Random Forest. In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP; ISBN 978-989-758-004-8; ISSN 2184-4321, SciTePress, pages 594-598. DOI: 10.5220/0004739005940598

@conference{visapp14,
author={Yohei Mishina. and Masamitsu Tsuchiya. and Hironobu Fujiyoshi.},
title={Boosted Random Forest},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP},
year={2014},
pages={594-598},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004739005940598},
isbn={978-989-758-004-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2014) - Volume 1: VISAPP
TI - Boosted Random Forest
SN - 978-989-758-004-8
IS - 2184-4321
AU - Mishina, Y.
AU - Tsuchiya, M.
AU - Fujiyoshi, H.
PY - 2014
SP - 594
EP - 598
DO - 10.5220/0004739005940598
PB - SciTePress