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Abstract: . Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new... (Update)
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BibTeX entry: (Update)
T.G. Dietterich. Ensemble methods in machine learning. In Multiple Classier Systems, Cagliari, Italy, 2000. http://citeseer.ist.psu.edu/dietterich00ensemble.html More
@article{ dietterich00ensemble,
author = "Thomas G. Dietterich",
title = "Ensemble Methods in Machine Learning",
journal = "Lecture Notes in Computer Science",
volume = "1857",
pages = "1-15",
year = "2000",
url = "citeseer.ist.psu.edu/dietterich00ensemble.html" }
Citations (may not include all citations):657 Bagging predictors - Breiman - 1996
509 A decision-theoretic generalization of on-line learning and .. - Freund, Schapire - 1995
500 Experiments with a new boosting algorithm - Freund, Schapire - 1996
243 Boosting the margin: A new explanation for the effectiveness.. - Schapire, Freund et al. - 1997
214 Improved boosting algorithms using confidence-rated predicti.. - Schapire, Singer - 1998
199 Probabilistic inference using Markov chain Monte Carlo metho.. - Neal - 1993
183 Solving multiclass learning problems via error-correcting ou.. - Dietterich, Bakiri - 1995
155 An empirical comparison of voting classification algorithms:.. - Bauer, Kohavi - 1999
134 An experimental comparison of three methods for constructing.. - Dietterich - 2000
102 Training a 3-node neural network is NPComplete - Blum, Rivest - 1988
102 Neural network ensembles (context) - Hansen, Salamon - 1990
94 Constructing optimal binary decision trees is NP-Complete (context) - Hyafil, Rivest - 1976
79 Error reduction through learning multiple descriptions - Ali, Pazzani - 1996
62 Universal approximation of an unknown mapping and its deriva.. (context) - Hornik, Stinchcombe et al. - 1990
61 Error correlation and error reduction in ensemble classifier.. - Tumer, Ghosh - 1996
57 Multiple decision trees (context) - Kwok, Carter - 1990
48 Back propagation is sensitive to initial conditions - Kolen, Pollack - 1991
27 Using output codes to boost multiclass learning problems - Schapire - 1997
24 Human expert-level performance on a scientific image analysi.. - Cherkauer - 1996
19 Bootstrapping with noise: An effective regularization techni.. - Raviv, Intrator - 1996
9 Improving committee diagnosis with resampling techniques (context) - Parmanto, Munro et al. - 1996
7 Extending local learners with error-correcting output codes - Ricci, Aha - 1997
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