Improved Boosting Algorithms Using Confidence-rated Predictions Export

@article{citeulike:2269430, 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.}, address = {Hingham, MA, USA}, author = {Schapire, Robert E. and Singer, Yoram}, citeulike-article-id = {2269430}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=337870}, citeulike-linkout-1 = {http://dx.doi.org/10.1023/A:1007614523901}, citeulike-linkout-2 = {http://www.springerlink.com/content/k8134wq0824k7042}, day = {1}, doi = {10.1023/A:1007614523901}, issn = {0885-6125}, journal = {Machine Learning}, keywords = {adaboost, boosting, multiclass}, month = {December}, number = {3}, pages = {297--336}, posted-at = {2008-01-21 17:17:11}, priority = {2}, publisher = {Kluwer Academic Publishers}, title = {Improved Boosting Algorithms Using Confidence-rated Predictions}, url = {http://dx.doi.org/10.1023/A:1007614523901}, volume = {37}, year = {1999} }

TY - JOUR ID - citeulike:2269430 L3 - citeulike-article-id:2269430 N2 - 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. IS - 3 JF - Machine Learning SN - 0885-6125 AD - Hingham, MA, USA EP - 336 TI - Improved Boosting Algorithms Using Confidence-rated Predictions PB - Kluwer Academic Publishers VL - 37 SP - 297 KW - adaboost KW - boosting KW - multiclass AU - Schapire, Robert AU - Singer, Yoram PY - 1999/12/01/ UR - http://dx.doi.org/10.1023/A:1007614523901 ER -