Improved boosting algorithms using confidence-rated predictions

作     者：Schapire, RE Singer, Y 

作者机构：AT&T Labs Res Shannon Lab Florham Pk NJ 07932 USA 

出 版 物：《MACHINE LEARNING》 (机器学习)

年 卷 期：1999年第37卷第3期

页      面：297-336页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：boosting algorithms multiclass classification output coding decision trees 

摘      要：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.