基于AUC界的无异常自优化
Anomaly-Free Self-Optimization via AUC Bounds
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中文总结 AI 辅助
针对异常数据稀缺导致模型选择困难的问题，提出将AUC上界作为可微的无异常目标，直接优化异常检测系统的连续参数（如集成权重和分数重缩放），实验表明该方法优于传统选择方法且对伪异常构造不敏感。
中文摘要 AI 辅助
异常是罕见的，且在开发期间异常数据往往不可用，这使得确定哪些异常检测模型和配置能够泛化到未见过的异常变得困难。近期方法通过生成伪异常并利用可达到的ROC曲线下面积（AUC）的界，从有限候选集中选择最优配置来应对这一挑战。相反，我们将AUC界用作一个可微的、无异常的目标函数，直接优化异常检测系统的连续参数。我们通过优化集成权重并引入一种可学习的分数重缩放机制来演示该框架，该机制自适应伪异常分数，使得优化能够超越预定义的候选集。跨多个数据集和嵌入模型的实验表明，AUC界优化相较于传统模型选择和基于开发集的参数选择取得了显著的性能提升。结果进一步表明，直接优化对伪异常构造的选择不那么敏感。
英文摘要
Anomalies are rare, and anomalous data are often unavailable during development, making it difficult to determine which anomaly detection models and configurations will generalize to unseen anomalies. Recent approaches address this challenge by generating pseudo-anomalies and using bounds on the achievable area under the ROC curve (AUC) to select the optimal configuration from a finite set of candidates. Instead, we use the AUC bound as a differentiable, anomaly-free objective for directly optimizing continuous parameters of anomaly detection systems. We demonstrate this framework by optimizing ensemble weights and introducing a learnable score-rescaling mechanism that adapts pseudo-anomaly scores, enabling optimization beyond a predefined candidate set. Experiments across multiple datasets and embedding models show that AUC-bound optimization achieves significant performance gains over conventional model selection and prior development-set-based parameter selection. The results further show that direct optimization is less sensitive to the choice of pseudo-anomaly construction.
发表机构
- Aalborg University（奥尔堡大学）
- Pioneer Centre for AI（先锋人工智能中心）
机构由 AI 辅助整理，请以论文原文为准。
