A Monte Carlo analysis of ensemble classification

In this paper we extend previous results providing a theoretical analysis of a new Monte Carlo ensemble classifier. The framework allows us to characterize the conditions under which the ensemble approach can be expected to outperform the single hypothesis classifier. Moreover, we provide a closed form expression for the distribution of the true ensemble accuracy, as well as of its mean and variance. We then exploit this result in order to analyze the expected error behavior in a particularly interesting case.

• Type: Conference
• Year: 2004
• Where: ICML
• Authors: Roberto Esposito, Lorenza Saitta
• Keywords: Ensemble Approach, ICML 2004, Machine Learning, Single Hypothesis Classifier, True Ensemble Accuracy