Algorithmics: theory & practice
Algorithmics: theory & practice
Machine Learning
A Monte Carlo analysis of ensemble classification
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Monte Carlo theory as an explanation of bagging and boosting
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Out of bootstrap estimation of generalization error curves in bagging ensembles
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Inference on the prediction of ensembles of infinite size
Pattern Recognition
Hypothesis diversity in ensemble classification
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
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Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this framework it is also possible to define a new ensemble classifier, whose accuracy probability distribution can be computed exactly. This paper has two goals: first, an experimental comparison between the theoretical predictions and experimental results; second, a systematic comparison between bagging and Monte Carlo ensemble classification.