Risk-Sensitive Learning via Minimization of Empirical Conditional Value-at-Risk(Artificial Intelligence and Cognitive Science)

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概要

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• 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.

• 社団法人電子情報通信学会の論文
• 2007-12-01

著者

• Kashima Hisashi

  Ibm Res. Yamato‐shi Jpn

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• Risk-Sensitive Learning via Minimization of Empirical Conditional Value-at-Risk(Artificial Intelligence and Cognitive Science)

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