Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation
Abstract
A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75 % compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.
Cite
Text
Sharma et al. "Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016. doi:10.1007/978-3-319-46131-1_25Markdown
[Sharma et al. "Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2016.](https://mlanthology.org/ecmlpkdd/2016/sharma2016ecmlpkdd-active/) doi:10.1007/978-3-319-46131-1_25BibTeX
@inproceedings{sharma2016ecmlpkdd-active,
title = {{Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation}},
author = {Sharma, Manali and Das, Kamalika and Bilgic, Mustafa and Matthews, Bryan L. and Nielsen, David and Oza, Nikunj C.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2016},
pages = {209-225},
doi = {10.1007/978-3-319-46131-1_25},
url = {https://mlanthology.org/ecmlpkdd/2016/sharma2016ecmlpkdd-active/}
}