[2Xin5] インタラクティブ1

[2Xin5-21] Feature selection using a few anomalies by reinforcement learning

〇Motonobu Uchikoshi¹, Koji Ichikawa¹, Yuka Aono¹, Takashi Urata² (1.The Japan Research Institute, Limited, 2.Sumitomo Mitsui Banking Corporation)

Keywords:anomaly detection, fraud detection, reinforcement learning

In anomaly detection, data other than known anomalies are often learned as normal data, and if the training data contains overlooked anomalies, the model performance may deteriorate. Especially when there are only a few known anomalies, it is difficult to automatically and efficiently extract overlooked anomalies. In order to extract the overlooked anomalies contained in the training data, we propose a reinforcement learning model that selects features so that known anomalies are separated into smaller clusters for the entire data. Experiments suggest that many of the known and overlooked anomalies are classified into smaller clusters, and that the model performance is improved by removing the clusters from the entire data to train the model. In addition, the selected features can be expected to provide interpretability to the known anomalies.