Explainable Sequential Anomaly Detection via Prototypes
Abstract
We propose EASD, a prototype-based framework for explainable sequential anomaly detection. By linking anomalous subsequences to representative prototypes, EASD provides human-understandable explanations while maintaining strong detection accuracy.
Type
Publication
In International Joint Conference on Neural Networks (IJCNN 2023)
Citation
@inproceedings{cheng2023explainable,
title={Explainable sequential anomaly detection via prototypes},
author={Cheng, He and Xu, Depeng and Yuan, Shuhan},
booktitle={2023 International Joint Conference on Neural Networks (IJCNN)},
pages={1--8},
year={2023},
organization={IEEE}
}
