无需标签自动选最优异常检测模型组合，提升效果还省计算。
Automatic Unsupervised Ensemble Outlier Model Selection--Extended Version
- 用元数据学习模型预测加新模型的增益，指导选择
- 在39个真实数据集上平均精度更高，用更少模型达更好效果
- 适合无标签场景下构建高效异常检测集成模型
无监督异常检测无需标注数据，具有吸引力；构建多模型集成可提高检测鲁棒性。然而，无标签条件下组建集成面临挑战：盲目组合易导致集成饱和，冗余或不可靠模型降低性能并增加计算开销。本文提出MetaEns，一种自动无监督集成模型选择框架。利用带标签的元数据集，MetaEns学习一个模型来预测边际集成增益，估算将候选模型加入部分构建的集成中所能带来的预期改进。测试时，该学习信号与受子模启发的代理目标结合，通过多样性感知折扣和族级风险正则化实现递增式选择，并支持自适应提前终止。结果表明，MetaEns能在无真实标签情况下构建紧凑且高质量的集成，在39个真实世界数据集上持续优于当前最先进的无监督选择器和集成基线，实现更高平均精度，同时使用更少模型。
原文摘要 · Abstract (English)
Unsupervised outlier detection is attractive because it eliminates the need for labeled data. Moreover, forming multi-model ensembles can improve detection robustness. However, composing an ensemble without labeled data is challenging. Naively composed ensembles can suffer from ensemble saturation, where redundant or unreliable detection models degrade performance and incur unnecessary computation. We propose MetaEns, an automatic unsupervised framework for selecting ensembles of outlier detection models. Using labeled meta-datasets, MetaEns learns a model that predicts marginal ensemble gains, estimating the expected improvement from adding a candidate model to a partially constructed ensemble. At test time, this learned signal is combined with a submodular-inspired proxy objective that enforces diminishing returns through diversity-aware discounting and family-level risk regularization, thereby enabling greedy sequential selection with adaptive early stopping. As a result, MetaEns constructs compact, high-quality ensembles without access to ground-truth labels. Experiments on 39 real-world datasets show that MetaEns consistently outperforms state-of-the-art unsupervised selectors and ensemble baselines, achieving higher average precision while using fewer models.
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