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Improved Boosting Algorithms Using Confidence-rated Predictions (1999)
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| Venue: | MACHINE LEARNING |
| Citations: | 938 - 26 self |
BibTeX
@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 single-label case which is simpler but as effective as techniques suggested by Freund and Schapire. Finally, we give some experimental results comparing a few of the algorithms discussed in this paper.
Keyphrases
boosting algorithm using confidence-rated prediction decision tree refined criterion third method several improvement single-label case weak hypothesis multi-label case new method adaboost boosting algorithm classification problem simplified analysis experimental result specific method improved parameter setting
