论文标题
在数据流上使用Mondrian P {ó} Lya森林的可解释的异常检测
Interpretable Anomaly Detection with Mondrian P{ó}lya Forests on Data Streams
论文作者
论文摘要
大规模检测是一个极具挑战性的实用性问题。当数据大且高维时，可能很难检测到哪些观察结果不符合预期的行为。最近的工作与（随机）$ k $ \ emph {d-Trees}的变化相结合以总结用于异常检测的数据。但是，这些方法依赖于不容易解释的临时分数函数，因此很难在没有标记的异常情况下选择检测到的异常的严重性或选择合理的阈值。为了解决这些问题，我们将这些方法在概率框架中进行环境化，我们称之为Mondrian \ polya {}森林，用于估算生成数据的潜在概率密度函数，并启用了比先前的工作更大的解释性。此外，我们开发了一个能够在现代流媒体环境中运行的内存有效变体。我们的实验表明，这些方法在提供统计上可解释的异常得分的同时，达到了最先进的性能。
Anomaly detection at scale is an extremely challenging problem of great practicality. When data is large and high-dimensional, it can be difficult to detect which observations do not fit the expected behaviour. Recent work has coalesced on variations of (random) $k$\emph{d-trees} to summarise data for anomaly detection. However, these methods rely on ad-hoc score functions that are not easy to interpret, making it difficult to asses the severity of the detected anomalies or select a reasonable threshold in the absence of labelled anomalies. To solve these issues, we contextualise these methods in a probabilistic framework which we call the Mondrian \Polya{} Forest for estimating the underlying probability density function generating the data and enabling greater interpretability than prior work. In addition, we develop a memory efficient variant able to operate in the modern streaming environments. Our experiments show that these methods achieves state-of-the-art performance while providing statistically interpretable anomaly scores.
