Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data
This work addresses the challenge of handling contaminated data in anomaly detection for domains like security and healthcare, offering an incremental improvement over existing methods.
The paper tackles the problem of anomaly detection with contaminated training data by proposing the Adaptive and Aggressive Rejection (AAR) method, which outperforms the state-of-the-art by 0.041 AUROC in experiments on image and tabular datasets.
Handling contaminated data poses a critical challenge in anomaly detection, as traditional models assume training on purely normal data. Conventional methods mitigate contamination by relying on fixed contamination ratios, but discrepancies between assumed and actual ratios can severely degrade performance, especially in noisy environments where normal and abnormal data distributions overlap. To address these limitations, we propose Adaptive and Aggressive Rejection (AAR), a novel method that dynamically excludes anomalies using a modified z-score and Gaussian mixture model-based thresholds. AAR effectively balances the trade-off between preserving normal data and excluding anomalies by integrating hard and soft rejection strategies. Extensive experiments on two image datasets and thirty tabular datasets demonstrate that AAR outperforms the state-of-the-art method by 0.041 AUROC. By providing a scalable and reliable solution, AAR enhances robustness against contaminated datasets, paving the way for broader real-world applications in domains such as security and healthcare.