Risk-sensitive learning via expected shortfall minimization

Abstract

A new approach for cost-sensitive classification is proposed. We extend the framework of cost-sensitive learning to mitigate risks of huge costs occurring with low probabilities, and propose an algorithm that achieves this goal. Instead of minimizing the expected cost commonly used in cost-sensitive learning, our algorithm minimizes expected shortfall, a.k.a. conditional value-at-risk, known as a good risk metric in the area of financial engineering. The proposed algorithm is a general meta-learning algorithm that can utilize existing example-dependent cost-sensitive learning algorithms, and is capable of dealing with not only alternative actions in ordinary classification tasks, but also allocative actions in resource-allocation type tasks.