AdaCost: Misclassification Cost-Sensitive Boosting

AdaCost, a variant of AdaBoost, is a misclassification cost-sensitive boosting method. It uses the cost of misclassifications to update the training distribution on successive boosting rounds. The purpose is to reduce the cumulative misclassification cost more than AdaBoost. We formally show that AdaCost reduces the upper bound of cumulative misclassification cost of the training set. Empirical evaluations have shown significant reduction in the cumulative misclassification cost over AdaBoost without consuming additional computing power.

• Type: Conference
• Year: 1999
• Where: ICML
• Authors: Wei Fan, Salvatore J. Stolfo, Junxin Zhang, Philip K. Chan
• Keywords: Cost-sensitive Boosting Method, Cumulative Misclassification Cost, ICML 1999, Machine Learning, Successive Boosting Rounds