Cost-Sensitive Learning to Rank

We formulate the Cost-Sensitive Learning to Rank problem of learning to prioritize limited resources to mitigate the most costly outcomes. We develop improved ranking models to solve this problem, as verified by experiments in diverse domains such as forest fire prevention, crime prevention, and preventing storm caused outages in electrical networks.

Authors

• Ryan McBride
• Ke Wang School of Astronautics, Beihang University; Tianmushan Laboratory, Beihang University
• Zhouyang Ren
• Wenyuan Li

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