Rie Kubota Ando
CoNLL 2006
We extend the framework of cost-sensitive classification 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 conditional value-at-risk, also known as expected shortfall, which is considered a good risk metric in the area of financial engineering. The proposed algorithm is a general meta-learning algorithm that can exploit 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. Experiments on tasks with example-dependent costs show promising results. Copyright © 2007 The Institute of Electronics.
Rie Kubota Ando
CoNLL 2006
Yale Song, Zhen Wen, et al.
IJCAI 2013
Annina Riedhauser, Viacheslav Snigirev, et al.
CLEO 2023
Joxan Jaffar
Journal of the ACM