Targeted Meta-Learning for Critical Incident Detection inWeather Data
Mohammad Mahdi Kamani, Sadegh Farhang, Mehrdad Mahdavi, James Z. Wang
The Pennsylvania State University
Abstract:
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