Towards Unsupervised Validation of Anomaly-Detection Models

• URL: http://arxiv.org/abs/2410.14579v1
• Date: Fri, 18 Oct 2024 16:27:04 GMT
• Title: Towards Unsupervised Validation of Anomaly-Detection Models
• Authors: Lihi Idan,
• Abstract summary: 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.
• Score: 5.439020425819001
• License:
• Abstract: 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.

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