Abstract
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.
Cite
CITATION STYLE
McBride, R., Wang, K., Ren, Z., & Li, W. (2019). Cost-sensitive learning to rank. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 (pp. 4570–4577). AAAI Press. https://doi.org/10.1609/aaai.v33i01.33014570
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