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.
Keywords: Boosting algorithms, multiclass classification, output coding, decision trees
