We address the problem of selecting members of ensembles for unsupervised outlier detection. The challenge here is to identify individually accurate but diverse members due to unsupervised nature of the problem. For this, we herein propose AnD-SELECT: Accurate-and-Diverse Selector, which considers a set of heterogeneous outlier detection methods at input and systematically selects accurate parameter variants i.e. parameterization of each type. Outlier detection methods in this input set are chosen such that they usually exhibit the characteristics of either progressive or regressive performance behavior with increasing parameter values. We then consider a wide range of parameter variants of each of these methods. From such homogeneous set of a method type, the objective is to select the more accurate parameterization-end, while avoiding selection of both the ends together due to above mentioned characteristics. Therefore, either a single accurate variant or a set of two variants showing explicit trade-off between accuracy and diversity, get selected. Evaluation on benchmark datasets shows notable performance improvement over existing selectors.

我们解决了选择合奏成员进行无监督离群值检测的问题。面临的挑战是，由于问题的不受监督的性质，要确定个体准确但多样化的成员。为此，我们在此提出AnD-SELECT：精确多样的选择器，它在输入时考虑了一组异类离群值检测方法，并系统地选择了精确的参数变量，即每种类型的参数化。选择此输入集中的异常值检测方法，使得它们通常随参数值的增加而呈现出渐进式或回归式性能行为的特征。然后，我们考虑每种方法的各种参数变体。从这种类型的方法类型的同类集合中，目标是选择更准确的参数化端，同时避免由于上述特性而同时选择两端。因此，选择一个准确的变体或两个变体的集合来显示精确度和多样性之间的显着权衡。