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2019 AAAI AAAI 2019

Cost-Sensitive Learning to Rank

Abstract

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.

🚀 Conference Pioneer — AAAI 2019
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — risk mitigation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Ryan McBride , Ke Wang , Zhouyang Ren , Wenyuan Li

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Risk Management Data Science & Analytics > Applications > Risk Management Machine Learning > Learning Types > Ranking

Keywords

risk management learning to rank resource allocation cost-sensitive learning ranking model risk mitigation high-cost outcome prediction prioritization model resource prioritization forest fire prevention
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