Towards Unsupervised Validation of Anomaly-Detection Models
This addresses a critical bottleneck for implementing automated anomaly-detection pipelines in domains where labeled validation data is unavailable.
The paper tackles the problem of validating anomaly-detection models without labeled data, presenting a new paradigm inspired by collaborative decision-making that achieves accurate and robust results for model selection and evaluation tasks.
Unsupervised validation of anomaly-detection models is a highly challenging task. While the common practices for model validation involve a labeled validation set, such validation sets cannot be constructed when the underlying datasets are unlabeled. The lack of robust and efficient unsupervised model-validation techniques presents an acute challenge in the implementation of automated anomaly-detection pipelines, especially when there exists no prior knowledge of the model's performance on similar datasets. This work presents a new paradigm to automated validation of anomaly-detection models, inspired by real-world, collaborative decision-making mechanisms. We focus on two commonly-used, unsupervised model-validation tasks -- model selection and model evaluation -- and provide extensive experimental results that demonstrate the accuracy and robustness of our approach on both tasks.