Cost-Sensitive Learning to Rank

Ryan McBride, Ke Wang, Zhouyang Ren, Wenyuan Li

AAAI 2019 pp. 4570-4577

doi:10.1609/AAAI.V33I01.33014570 /aaai/2019/mcbride2019aaai-cost/

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.

PDF AAAI Semantic Scholar

Cite

Text

McBride et al. "Cost-Sensitive Learning to Rank." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.33014570

Markdown

[McBride et al. "Cost-Sensitive Learning to Rank." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/mcbride2019aaai-cost/) doi:10.1609/AAAI.V33I01.33014570

BibTeX

@inproceedings{mcbride2019aaai-cost,
  title     = {{Cost-Sensitive Learning to Rank}},
  author    = {McBride, Ryan and Wang, Ke and Ren, Zhouyang and Li, Wenyuan},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {4570-4577},
  doi       = {10.1609/AAAI.V33I01.33014570},
  url       = {https://mlanthology.org/aaai/2019/mcbride2019aaai-cost/}
}