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.

Experimental Comparison between Bagging and Monte Carlo Ensemble Classification

ESPOSITO, Roberto;
2005-01-01

Abstract

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.
2005
ICML - INT. CONF. ON MACHINE LEARNING
Bonn, Germany
11-13 August 2005
Proceedings of the twenty-second International Conference of Machine Learning
ACM
119
209
216
9781595931801
http://portal.acm.org/citation.cfm?id=1102351.1102378&coll=ACM&dl=ACM&CFID=6518204&CFTOKEN=51745247
Montecarlo algorithm; ensemble learning; bagging; boosting
R. ESPOSITO; L. SAITTA
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/19925
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