论文标题
通过总产品网络的异常说明
Outlier Explanation via Sum-Product Networks
论文作者
论文摘要
异常说明是确定将样本与正常数据区分开的一组功能的任务，这对于下游（人）决策很重要。现有方法基于特征子集的空间中的光束搜索。它们在计算上很快变得昂贵，因为他们需要为每个功能子集从头开始运行异常检测算法。为了减轻这个问题，我们提出了一种基于总和 - 产品网络（SPNS）（一类概率电路）的新型离群解释算法。我们的方法利用了SPN中边际推断的障碍来计算特征子集中的离群分数。通过使用SPN，可以向后消除而不是通常的前向光束搜索，这是可行的，这不太容易在解释中缺少相关功能，尤其是当功能数量较大时。我们从经验上表明，我们的方法取得了最先进的结果，以实现异常说明，表现优于近期基于搜索和深度学习的解释方法
Outlier explanation is the task of identifying a set of features that distinguish a sample from normal data, which is important for downstream (human) decision-making. Existing methods are based on beam search in the space of feature subsets. They quickly becomes computationally expensive, as they require to run an outlier detection algorithm from scratch for each feature subset. To alleviate this problem, we propose a novel outlier explanation algorithm based on Sum-Product Networks (SPNs), a class of probabilistic circuits. Our approach leverages the tractability of marginal inference in SPNs to compute outlier scores in feature subsets. By using SPNs, it becomes feasible to perform backwards elimination instead of the usual forward beam search, which is less susceptible to missing relevant features in an explanation, especially when the number of features is large. We empirically show that our approach achieves state-of-the-art results for outlier explanation, outperforming recent search-based as well as deep learning-based explanation methods
