1999 ImprovedBoostingAlgorithmsUsing

(Schapire et al., 1999) ⇒ Robert E. Schapire, and Yoram Singer. (1999). “Improved Boosting Algorithms Using Confidence-rated Predictions.” In: Machine Learning, 37(3). doi:10.1023/A:1007614523901

Subject Headings: AdaBoost Algorithm, Multiclass Classification Algorithm.

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Author Keywords

boosting algorithms, multiclass classification, output coding, decision trees

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
1999 ImprovedBoostingAlgorithmsUsing	Robert E. Schapire Yoram Singer			Improved Boosting Algorithms Using Confidence-rated Predictions				10.1023/A:1007614523901		1999