Anomaly Detection with Adaptive and Aggressive Rejection for Contaminated Training Data

TL;DR

This paper introduces AAR, a dynamic anomaly rejection method that improves robustness in contaminated training data for anomaly detection, outperforming existing approaches across multiple datasets.

Contribution

The paper presents a novel adaptive and aggressive rejection technique that dynamically excludes anomalies, addressing limitations of fixed contamination ratio assumptions in traditional methods.

Findings

AAR outperforms state-of-the-art methods by 0.041 AUROC.

AAR effectively balances normal data preservation and anomaly exclusion.

The method demonstrates robustness across diverse image and tabular datasets.

Abstract

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.