Online Anomaly Detection via Class-Imbalance Learning
This work addresses anomaly detection in online settings for applications like fraud detection, offering an incremental improvement in handling class imbalance.
The authors tackled online anomaly detection by maximizing the Gmean metric through a convex surrogate loss, resulting in performance comparable to a recent cost-sensitive online classification algorithm across various datasets while maintaining low runtime.
Anomaly detection is an important task in many real world applications such as fraud detection, suspicious activity detection, health care monitoring etc. In this paper, we tackle this problem from supervised learning perspective in online learning setting. We maximize well known \emph{Gmean} metric for class-imbalance learning in online learning framework. Specifically, we show that maximizing \emph{Gmean} is equivalent to minimizing a convex surrogate loss function and based on that we propose novel online learning algorithm for anomaly detection. We then show, by extensive experiments, that the performance of the proposed algorithm with respect to $sum$ metric is as good as a recently proposed Cost-Sensitive Online Classification(CSOC) algorithm for class-imbalance learning over various benchmarked data sets while keeping running time close to the perception algorithm. Our another conclusion is that other competitive online algorithms do not perform consistently over data sets of varying size. This shows the potential applicability of our proposed approach.