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We present a simple unified framework for multi-class cost-sensitiveboosting. The minimum-risk class is estimated directly, rather than via anapproximation of the posterior distribution. Our method jointly optimizesbinary weak learners and their corresponding output vectors, requiring classesto share features at each iteration. By training in a cost-sensitive manner,weak learners are invested in separating classes whose discrimination isimportant, at the expense of less relevant classification boundaries.Additional contributions are a family of loss functions along with proof thatour algorithm is Boostable in the theoretical sense, as well as an efficientprocedure for growing decision trees for use as weak learners. We evaluate ourmethod on a variety of datasets: a collection of synthetic planar data, commonUCI datasets, MNIST digits, SUN scenes, and CUB-200 birds. Results showstate-of-the-art performance across all datasets against several strongbaselines, including non-boosting multi-class approaches.

Improved Multi-Class Cost-Sensitive Boosting via Estimation of the Minimum-Risk Class