1. Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection
- Author
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Masakuna, Jordan F., Nkashama, DJeff Kanda, Soltani, Arian, Frappier, Marc, Tardif, Pierre-Martin, and Kabanza, Froduald
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - Abstract
Training data sets intended for unsupervised anomaly detection, typically presumed to be anomaly-free, often contain anomalies (or contamination), a challenge that significantly undermines model performance. Most robust unsupervised anomaly detection models rely on contamination ratio information to tackle contamination. However, in reality, contamination ratio may be inaccurate. We investigate on the impact of inaccurate contamination ratio information in robust unsupervised anomaly detection. We verify whether they are resilient to misinformed contamination ratios. Our investigation on 6 benchmark data sets reveals that such models are not adversely affected by exposure to misinformation. In fact, they can exhibit improved performance when provided with such inaccurate contamination ratios., Comment: This is an accepted extended abstract at Black in AI Workshop which will be co-located with NeurIPS 2024 in Canada
- Published
- 2024