离群点检测算法的评价指标

随着离群点检测技术的深入研究和广泛应用，越来越多的优秀算法被提出来，然而，现有的离群点检测技术的评价仍然沿用传统分类算法的测量指标，存在着评价指标单一、适应性差的问题。针对这些问题，提出了一类高真正率指标（HT_AUC）和二类低假正率指标（LF_AUC）。首先，整理常用的离群点检测评价指标，分析其优缺点和适用场景；然后，在已有的曲线下面积（AUC）方法的基础上，分别针对高真正率（TPR）要求和低假正率（FPR）要求，提出了一类高真正率指标和二类低假正率指标，为离群点检测算法的效果评价和量化集成提供了更合适的指标。在真实数据集上的实验结果表明，与传统评价指标的相比，所提出的方法更能满足一类高真正率和二类低假正率要求。

Evaluation metrics of outlier detection algorithms

With the in-depth research and extensive application of outlier detection technology, more and more excellent algorithms have been proposed. However, the existing outlier detection algorithms still use the evaluation metrics of traditional classification, which leads to the problems of singleness and poor adaptability of evaluation metrics. To solve these problems, the first type of High True positive rate-Area Under Curve (HT_AUC) and the second type of Low False positive rate-Area Under Curve (LF_AUC) were proposed. First, the commonly used outlier detection evaluation metrics were analyzed to illustrate their advantages and disadvantages as well as applicable scenarios. Then, based on the existing Area Under Curve (AUC) method, the HT_AUC and the LF_AUC were proposed aiming at the high True Positive Rate (TPR) demand and low False Positive Rate (FPR) demand respectively, so as to provide more suitable metrics for performance evaluation as well as quantization and integration of outlier detection algorithms. Experimental results on real-world datasets show that the proposed method is able to better satisfy the demands of the first type of high true rate and the second type of low false positive rate than the traditional evaluation metrics.