In unsupervised outlier ensembles, the absence of ground truth makes the combination of base detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we propose a framework---called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)---which addresses this issue by defining a local region around a test instance using the consensus of its nearest neighbors in randomly generated feature spaces. The top-performing base detectors in this local region are selected and combined as the model's final output. Four variants of the LSCP framework are compared with six widely used combination algorithms for parallel ensembles. Experimental results demonstrate that one of these LSCP variants consistently outperforms baseline algorithms on the majority of eighteen real-world datasets.

本文提出了一种名为LSCP的框架，通过在随机选择的特征子空间中使用其最近邻的共识来定义测试实例周围的本地区域，从而选择并组合最优基础检测器作为模型的最终输出，并比较了四种LSCP框架与七种广泛使用的平行框架的实验结果，其中一种变体LSCP_AOM在大部分20个真实世界数据集上表现优于基线。

LSCP: 并行异常集成中的本地选择性组合