Achieving Counterfactual Explanation for Sequence Anomaly Detection
CFDet framework illustrationAbstract
We propose CFDet, a counterfactual explanation framework for sequence anomaly detection. CFDet identifies anomalous entries by generating minimal and plausible modifications that alter a model’s prediction from anomalous to normal. Experiments on BGL, Thunderbird, and CERT datasets demonstrate that CFDet produces high-fidelity explanations and consistently outperforms attention-based, Shapley value, and gradient-based baselines.
Type
Publication
In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024)
Citation
@inproceedings{cheng2024cfdet,
title = {Achieving Counterfactual Explanation for Sequence Anomaly Detection},
author = {Cheng, He and Xu, Depeng and Yuan, Shuhan and Wu, Xintao},
booktitle = {Machine Learning and Knowledge Discovery in Databases. Research Track (ECML PKDD 2024)},
series = {Lecture Notes in Artificial Intelligence},
volume = {14948},
pages = {19--35},
publisher = {Springer},
year = {2024},
doi = {10.1007/978-3-031-70371-3_2}
}
