面向异常检测模型的无监督验证
Towards Unsupervised Validation of Anomaly-Detection Models
AI总结：
本文受现实协作决策机制启发，提出一种新的无监督异常检测模型验证范式，在模型选择与模型评估两项任务上通过大量实验证明了其准确性和稳健性。
摘要：
异常检测模型的无监督验证是一项极具挑战性的任务。虽然模型验证的常见做法涉及带标签的验证集，但当底层数据集未标注时，此类验证集无法构建。缺乏稳健且高效的无监督模型验证技术，给自动化异常检测流程的实现带来了严峻挑战，尤其是在没有关于模型在相似数据集上性能的先验知识时。本工作受现实世界协作决策机制的启发，提出了一种自动化验证异常检测模型的新范式。我们聚焦于两个常用的无监督模型验证任务——模型选择和模型评估——并提供了大量实验结果，证明了我们的方法在这两项任务上的准确性和稳健性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
