Experimental comparison between bagging and Monte Carlo ensemble classification

Roberto Esposito; Lorenza Saitta

doi:10.1145/1102351.1102378

Experimental comparison between bagging and Monte Carlo ensemble classification

Roberto Esposito
, Lorenza Saitta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this frame-work 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.

Original language	English
Title of host publication	ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning
Editors	L. Raedt, S. Wrobel
Pages	209-216
Number of pages	8
DOIs	https://doi.org/10.1145/1102351.1102378
Publication status	Published - 2005
Externally published	Yes
Event	ICML 2005: 22nd International Conference on Machine Learning - Bonn, Germany Duration: 7 Aug 2005 → 11 Aug 2005

Publication series

Name	ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Conference

Conference	ICML 2005: 22nd International Conference on Machine Learning
Country/Territory	Germany
City	Bonn
Period	7/08/05 → 11/08/05

Access to Document

10.1145/1102351.1102378

Cite this

@inproceedings{41a8f57a510746478ba1a6be9b888f8f,

title = "Experimental comparison between bagging and Monte Carlo ensemble classification",

abstract = "Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this frame-work 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.",

author = "Roberto Esposito and Lorenza Saitta",

year = "2005",

doi = "10.1145/1102351.1102378",

language = "English",

isbn = "1595931805",

series = "ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning",

pages = "209--216",

editor = "L. Raedt and S. Wrobel",

booktitle = "ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning",

note = "ICML 2005: 22nd International Conference on Machine Learning ; Conference date: 07-08-2005 Through 11-08-2005",

}

Esposito, R & Saitta, L 2005, Experimental comparison between bagging and Monte Carlo ensemble classification. in L Raedt & S Wrobel (eds), ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning. ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning, pp. 209-216, ICML 2005: 22nd International Conference on Machine Learning, Bonn, Germany, 7/08/05. https://doi.org/10.1145/1102351.1102378

Experimental comparison between bagging and Monte Carlo ensemble classification. / Esposito, Roberto; Saitta, Lorenza.
ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning. ed. / L. Raedt; S. Wrobel. 2005. p. 209-216 (ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Esposito, Roberto

AU - Saitta, Lorenza

PY - 2005

Y1 - 2005

N2 - Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this frame-work 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.

AB - Properties of ensemble classification can be studied using the framework of Monte Carlo stochastic algorithms. Within this frame-work 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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DO - 10.1145/1102351.1102378

M3 - Conference contribution

AN - SCOPUS:31844451707

SN - 1595931805

T3 - ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

SP - 209

EP - 216

BT - ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

A2 - Raedt, L.

A2 - Wrobel, S.

T2 - ICML 2005: 22nd International Conference on Machine Learning

Y2 - 7 August 2005 through 11 August 2005

ER -

Experimental comparison between bagging and Monte Carlo ensemble classification

Abstract

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Conference

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