COPOD：基于Copula的离群点检测
COPOD: Copula-Based Outlier Detection
摘要：
离群值检测是指识别偏离一般数据分布的罕见项目。现有的方法存在计算复杂度高、预测能力低和可解释性有限的问题。作为一种补救措施，我们提出了一种新的离群点检测算法COPOD，这是受Copula建模多元数据分布。COPOD首先构造一个经验Copula函数，然后用它来预测每个给定数据点的尾部概率，以确定其“极值”水平。直觉上，我们认为这是计算一个异常的p值。这使得COPOD既无参数，高度可解释，计算效率。在这项工作中，我们做出了三个关键贡献，1）提出了一种新的，无参数的离群值检测算法，具有很好的性能和可解释性，2）在30个基准数据集上进行了广泛的实验，以表明COPOD在大多数情况下都优于其他算法，也是最快的算法之一，3）发布了一个易于使用的Python实现，以实现可重复性。
英文摘要：
Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of "extremeness". Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
