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Improved Boosting Algorithms Using Confidence-rated Predictions (1999) [407 citations — 19 self]

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@MISC{Schapire99improvedboosting,
    author = {Robert E. Schapire and Yoram Singer},
    title = {Improved Boosting Algorithms Using Confidence-rated Predictions},
    year = {1999}
}

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Abstract:

. We describe several improvements to Freund and Schapire's AdaBoost boosting algorithm, particularly in a setting in which hypotheses may assign confidences to each of their predictions. We give a simplified analysis of AdaBoost in this setting, and we show how this analysis can be used to find improved parameter settings as well as a refined criterion for training weak hypotheses. We give a specific method for assigning confidences to the predictions of decision trees, a method closely related to one used by Quinlan. This method also suggests a technique for growing decision trees which turns out to be identical to one proposed by Kearns and Mansour. We focus next on how to apply the new boosting algorithms to multiclass classification problems, particularly to the multi-label case in which each example may belong to more than one class. We give two boosting methods for this problem, plus a third method based on output coding. One of these leads to a new method for handling the singl...

Citations

2253	UCI Repository of machine learning databases – Blake, Merz - 1998
1228	A decision-theoretic generalization of on-line learning and application to boosting”, Computational Learning Theory: Eurocolt’95 – Freund, Schapire - 1995
1068	Experiments with a new boosting algorithm – Freund, Schapire - 1996
619	Additive Logistic Regression: a Statistical View of Boosting – Friedman, Hastie, et al. - 2000
507	Boosting the margin: a new explanation for the effectiveness of voting methods – Schapire, Freund, et al. - 1998
348	Solving multiclass learning problems via error-correcting output codes – Dietterich, Bakiri - 1995
302	Decision theoretic generalizations of the PAC model for neural net and other learning applications – Haussler - 1992
284	D.: What Size Net Gives Valid Generalization – Baum, Haussler - 1989
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223	Bagging, boosting, and C4.5 – Quinlan - 1996
201	Arcing classifier – Breiman - 1998
131	The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network – Bartlett - 1998
107	Practical Methods of Optimization, Second Edition – Fletcher - 2003
106	Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain – Blum - 1997
79	C.: Boosting decision trees – Drucker, Cortes - 1995
76	Pruning adaptive boosting – Margineantu, Dietterich - 1997
73	An empirical evaluation of bagging and boosting – Maclin, Opitz - 1997
68	Using output codes to boost multiclass learning problems – Schapire
65	On the boosting ability of top-down decision tree learning algorithms – Kearns, Mansour - 1996
58	Using and combining predictors that specialize – Freund, Schapire, et al. - 1997
31	A generalization of Sauer’s lemma – Haussler, Long - 1995
4	Information geometry and alternaning minimization procedures – Csiszar, Tusnady - 1984