Experiments with a New Boosting Algorithm-英文文献.pdf

Machine Learning: Proceedings of the Thirteenth International Conference, 1996. Experiments with a New Boosting Algorithm Yoav Freund Robert E. Schapire ATT Laboratories 600 Mountain Avenue Murray Hill, NJ 07974-0636 fyoav, schapireg@ Abstract. In an earlier paper, we introduced a new “boosting” This paper describes two distinct sets of experiments. algorithm called AdaBoost which, theoretically, can be used to In the first set of experiments, described in Section 3, we significantly reduce the error of any learning algorithm that con- compared boosting to “bagging,” a method described by sistently generates classifiers whose performance is a little better Breiman [1] which works in the same general fashion (i.e., than random guessing. We also introduced the related notion of a by repeatedly rerunning a given weak learning algorithm, “pseudo-loss” which is a method for forcing a learning algorithm and combining the computed classifiers), but which con- of multi-label conceptsto concentrate on the labels that are hardest to discriminate. In this paper, we describe experiments we carried structs each distributionin a simpler manner. (Details given out to assess how well AdaBoost with and without pseudo-loss, below.) We compared boosting with bagging because both performs on real learning problems. methods work by combining many classifiers. This com- We performed two sets of experiments. The first set compared