Targeted Meta-Learning for Critical Incident Detection inWeather Data

Mohammad Mahdi Kamani, Sadegh Farhang, Mehrdad Mahdavi, James Z. Wang
The Pennsylvania State University

Due to imbalanced or heavy-tailed nature of weather- and climate-related datasets, the performance of standard deep learning models significantly deviates from their expected behavior on test data. Classical methods to address these issues are mostly data or application dependent, hence burdensome to tune. Meta-learning approaches, on the other hand, aim to learn hyperparameters in the learning process using different objective functions on training and validation data. However, these methods suffer from high computational complexity and are not scalable to large datasets. In this paper, we aim to apply a novel framework named as targeted meta-learning to rectify this issue, and show its efficacy in dealing with the aforementioned biases in datasets. This framework employs a small, well-crafted target dataset that resembles the desired nature of test data in order to guide the learning process in a coupled manner. We empirically show that this framework can overcome the bias issue, common to weather-related datasets, in a bow echo detection case study.

Full Paper
(PDF, 2MB)

More information

Citation: Mohammad M. Kamani, Sadegh Farhang, Mehrdad Mahdavi and James Z. Wang, ``Targeted Meta-Learning for Critical Incident Detection in Weather Data,'' Proceedings of the Workshop at the International Conference on Machine Learning, Climate Change: How Can AI Help?, pp. -, Long Beach, California, June 2019.

© 2019 Authors. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the authors.

Last Modified: June 7, 2019
© 2019