Interactive Anomaly Identification with Erroneous Feedback

The difficulties in analyzing large and extensive systems necessitate the use of efficient machine-learning tools to identify unknown system anomalies in order to avoid critical problems and ensure high reliability. Given that data logged by a system include unknown anomalies, anomaly identification models aim to simultaneously identify the time of occurrence and the features that contributed to these anomalies. To maximize accuracy, it is important to utilize the data as well as the domain knowledge of the system. However, it is difficult for a system analyst to possess not only machine-learning capabilities but also domain knowledge to incorporate into the model. In this paper, we propose a new anomaly identification framework capable of utilizing feedback based on domain knowledge without requiring any machine-learning capabilities. We also propose a novel method, the so-called rank ensemble method, to improve the accuracy of anomaly identification with erroneous feedback, that is, feedback that includes incorrect information. Our method enables erroneous information to be adaptively ignored by assuming consistency between the data and the user feedback. An intensive parameter study using benchmark datasets and a case study with real vehicle data demonstrate the applicability of our framework.