Improved Boosting Algorithms Using Confidencerated Predictions
 Robert E. Schapire,
 Yoram Singer
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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 multilabel 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 singlelabel 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.
 Title
 Improved Boosting Algorithms Using Confidencerated Predictions
 Journal

Machine Learning
Volume 37, Issue 3 , pp 297336
 Cover Date
 199912
 DOI
 10.1023/A:1007614523901
 Print ISSN
 08856125
 Online ISSN
 15730565
 Publisher
 Kluwer Academic Publishers
 Additional Links
 Topics
 Keywords

 boosting algorithms
 multiclass classification
 output coding
 decision trees
 Industry Sectors
 Authors

 Robert E. Schapire ^{(1)}
 Yoram Singer ^{(1)}
 Author Affiliations

 1. Shannon Laboratory, AT&T Labs, 180 Park Avenue, Room A279, Florham Park, NJ, 079320971, USA