Impact of Inaccurate Contamination Ratios on Robust Unsupervised Anomaly Detection: Experimental Investigation

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, the contamination ratio may be inaccurate. We investigate the impact of inaccurate contamination ratio information in robust unsupervised anomaly detection. We verify whether they are resilient to misinformed contamination ratios. It appears that there is a lack of discussion in the literature regarding the impact of incorrect contamination ratio information on unsupervised anomaly detection, despite its critical importance. Our investigation on 8 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.