Authors
Simon Bernard, Sébastien Adam, Laurent Heutte
Publication date
2007/9/23
Conference
Ninth international conference on document analysis and recognition (ICDAR 2007)
Volume
2
Pages
1043-1047
Publisher
IEEE
Description
In the pattern recognition field, growing interest has been shown in recent years for multiple classifier systems and particularly for bagging, boosting and random sub-spaces. Those methods aim at inducing an ensemble of classifiers by producing diversity at different levels. Following this principle, Breiman has introduced in 2001 another family of methods called random forest. Our work aims at studying those methods in a strictly pragmatic approach, in order to provide rules on parameter settings for practitioners. For that purpose we have experimented the forest-RI algorithm, considered as the random forest reference method, on the MNIST handwritten digits database. In this paper, we describe random forest principles and review some methods proposed in the literature. We present next our experimental protocol and results. We finally draw some conclusions on random forest global behavior according to their …
Total citations
Scholar articles
S Bernard, S Adam, L Heutte - Ninth international conference on document analysis …, 2007