Distributional Measurement-based Model Selection for Anomaly Detection

Anomaly detection (AD) is a fundamental task in data analysis with broad applications in high-stakes domains. Although numerous AD algorithms have been developed over the past decades, this rich methodological landscape makes detector selection increasingly challenging. Existing automated AD methods mainly rely on hyperparameter optimization or meta-learning. Hyperparameter optimization (HO) is fully unsupervised and requires no historical tasks, but its effectiveness depends on surrogate internal criteria and it is often computationally expensive for large-scale datasets. Meta-learning methods transfer selection knowledge from historical tasks by constructing task-level meta-features, yet existing approaches primarily use coarse statistical summaries, which may fail to capture subtle structural and relational patterns that are critical in complex AD tasks. To address these limitations, we propose a distribution-aware meta-learning framework for automated AD model selection. Instead of representing each dataset only by statistical summaries, our method constructs informative and task-guided meta-features through distributional measurements across datasets. Specifically, we first build sample-wise similarity graphs to map heterogeneous datasets into a unified relational representation space. We then compute graph-based distances or similarities between datasets, thereby capturing fine-grained structural and distributional information. Extensive experiments across diverse AD tasks and two modalities show that our method consistently achieves superior or comparable detection performance compared with existing model selection techniques, while substantially reducing online selection time compared with the HO-based approaches. Moreover, the constructed meta-features are robust to domain shifts, allowing our framework to select strong detectors for new tasks from domains not observed in the historical task set.