公开(公告)号：US20230274154A1

公开(公告)日：2023-08-31

申请号：US18113267

申请日：2023-02-23

Applicant: Google LLC

Inventor： Jinsung Yoon ,  Sercan Omer Arik ,  Madeleine Richards Udell ,  Chun-Hao Chang

IPC: G06N3/09 ,  G06N3/088 ,  G06N3/0895

CPC classification number: G06N3/09 ,  G06N3/088 ,  G06N3/0895

Abstract: Aspects of the disclosure provide for interpretable anomaly detection using a generalized additive model (GAM) trained using unsupervised and supervised learning techniques. A GAM is adapted to detect anomalies using an anomaly detection partial identification (AD PID) loss function for handling noisy or heterogeneous features in model input. A semi-supervised data interpretable anomaly detection (DIAD) system can generate more accurate results over models trained for anomaly detection using strictly unsupervised techniques. In addition, output from the DIAD system includes explanations, for example as graphs or plots, of relatively important input features that contribute to the model output by different factors, providing interpretable results from which the DIAD system can be improved upon.