ML Anthology

Experimental Comparison Between Bagging and Monte Carlo Ensemble Classification

Roberto Esposito, Lorenza Saitta
ICML 2005 pp. 209-216
doi:10.1145/1102351.1102378 /icml/2005/esposito2005icml-experimental/

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.

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Cite

Text

Esposito and Saitta. "Experimental Comparison Between Bagging and Monte Carlo Ensemble Classification." International Conference on Machine Learning, 2005. doi:10.1145/1102351.1102378

Markdown

[Esposito and Saitta. "Experimental Comparison Between Bagging and Monte Carlo Ensemble Classification." International Conference on Machine Learning, 2005.](https://mlanthology.org/icml/2005/esposito2005icml-experimental/) doi:10.1145/1102351.1102378

BibTeX

@inproceedings{esposito2005icml-experimental,
  title     = {{Experimental Comparison Between Bagging and Monte Carlo Ensemble Classification}},
  author    = {Esposito, Roberto and Saitta, Lorenza},
  booktitle = {International Conference on Machine Learning},
  year      = {2005},
  pages     = {209-216},
  doi       = {10.1145/1102351.1102378},
  url       = {https://mlanthology.org/icml/2005/esposito2005icml-experimental/}
}