Risk-Sensitive Learning via Minimization of Empirical Conditional Value-at-Risk
Risk-Sensitive Learning via Minimization of Empirical Conditional Value-at-Risk
复制标题
通过最小化经验条件风险值进行风险敏感学习
DOI：
10.1093/ietisy/e90-d.12.2043
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
H. Kashima
中科院分区：
文献类型：
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作者：
H. Kashima
我们扩展了成本敏感分类的框架，以减轻低概率发生巨大成本的风险，并提出了一种实现这一目标的算法。我们的算法不是最小化成本敏感学习中常用的预期成本，而是最小化条件风险价值，也称为预期缺口，这被认为是金融工程领域的一个很好的风险度量。该算法是一个通用的元学习算法，可以利用现有的示例相关的成本敏感的学习算法，并能够处理不仅在普通分类任务的替代行动，但也分配行动的资源分配类型的任务。在具有示例依赖成本的任务上的实验显示了良好的结果。
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.