Forest-RK: A New Random Forest Induction Method
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A Study of Random Linear Oracle Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
On the selection of decision trees in random forests
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Towards a better understanding of random forests through the study of strength and correlation
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Mining data with random forests: A survey and results of new tests
Pattern Recognition
Pattern Recognition Letters
A parts-based multi-scale method for symbol recognition
GREC'11 Proceedings of the 9th international conference on Graphics Recognition: new trends and challenges
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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. Fol- lowing this principle, Breiman has introduced in 2001 an- other family of methods called Random Forest. Our work aims at studying those methods in a strictly pragmatic ap- proach, 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 ref- erence method, on the MNIST handwritten digits database. In this paper, we describe Random Forest principles and re- view some methods proposed in the literature. We present next our experimental protocol and results. We finally draw some conclusions on Random Forest global behavior ac- cording to their parameter tuning.