Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models

One such application is intrusion detection in computer systems, where data points are typically digital messages transmitted over a network, and messages that are detected as outliers are considered likely to carry a threat [13, 17]. Another application is obstacle detection in autonomous car driving [18]. The ability to detect outliers is also important in scientific applications, where points detected as such are intrinsically more interesting than inliers, and should therefore be given more attention [59, 28]. A number of techniques can be used for outlier detection [12, 21, 36, 41, 51]. In practice, it is not only important to be able to detect outliers and inliers with high accuracy, one would also like to be able to explain why a machine learning model considers a sample as inlier or outlier. An interpretable explanatory feedback can indeed be used by a human operator for appropriate decision making. The data point could either be considered as benign and possibly incorporated to the dataset, or appropriate action might be taken. The problem of outlier explanation is shown schematically in Figure 1.