Systems and methods for outlier detection using unsupervised machine learning models trained on balanced data

Abstract

Methods and systems are described herein for outlier detection. The system may apply a single-tier including a minority expert model only or a two-tier machine learning model, including a majority expert model and a minority expert model. The system may generate the minority expert model by training an unsupervised machine learning model on oversampled training data, including synthetic sample outlier events. In some embodiments, the minority expert model may provide a binary result indicating an event belongs to an outlier category or not. In some embodiments, the minority expert model may include multiple component models providing a multi-class result indicating whether an event belongs to a sub-category of the outlier category. In application, the system may perform the outlier detection on events that are sequence-based or not.