document: An iterative method for multi-class cost-sensitive learning Cost-sensitive learning addresses the issue of classification in the presence of varying costs associated with different types of misclassification . In this paper , we present a method for solving multi-class cost-sensitive learning problems using any binary classification algorithm . This algorithm is derived using hree key ideas : 1 ) iterative weighting ; 2 ) expanding data space ; and 3 ) gradient boosting with stochastic ensembles . We establish some theoretical guarantees concerning the performance of this method . In particular , we show that a certain variant possesses the boosting property , given a form of weak learning assumption on the component binary classifier . We also empirically evaluate the performance of the proposed method using benchmark data sets and verify that our method generally achieves better results than representative methods for cost-sensitive learning , in terms of predictive performance ( cost minimization ) and , in many cases , computational efficiency .
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