Efficient Top Rank Optimization with Gradient Boosting for Supervised Anomaly Detection

摘要

In this paper we address the anomaly detection problem in a supervised setting where positive examples might be very sparse. We tackle this task with a learning to rank strategy by optimizing a dif-ferentiable smoothed surrogate of the so-called Average Precision (AP). Despite its non-convexity, we show how to use it efficiently in a stochastic gradient boosting framework. We show that using AP is much better to optimize the top rank alerts than the state of the art measures. We demonstrate on anomaly detection tasks that the interest of our method is even reinforced in highly unbalanced scenarios.