Explainable and robust deep semi-supervised model for multi-class anomaly detection in flight data
Description
This model is a semi-supervised deep learning based anomaly detection for aircraft flight data. It is designed to work when a small subset of data is reviewed and labeled by experts. The most useful realm is where the size of labeled data is small, so that any supervised learning approach won't reach optimum performance.
Detailed example
anomaly detection
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This model is a semi-supervised deep learning based anomaly detection for aircraft flight data. It is designed to work when a small subset of data is reviewed and labeled by experts. The most useful realm is where the size of labeled data is small, so that any supervised learning approach won't reach optimum performance.
Controls / human review
ATO: Not reported; PIA: Not published