Efficient estimation of AUC in a sliding window
This work addresses a computational bottleneck for real-time monitoring in applications like change detection, offering an incremental improvement with practical efficiency gains.
The paper tackles the problem of efficiently computing the area under the ROC curve (AUC) in a sliding window over data streams, which is computationally expensive for large windows, by proposing an algorithm that estimates AUC within an error bound of ε/2 and achieves a per-update time of O((log k)/ε), providing significant speed-ups over the exact O(k) time method.
In many applications, monitoring area under the ROC curve (AUC) in a sliding window over a data stream is a natural way of detecting changes in the system. The drawback is that computing AUC in a sliding window is expensive, especially if the window size is large and the data flow is significant. In this paper we propose a scheme for maintaining an approximate AUC in a sliding window of length $k$. More specifically, we propose an algorithm that, given $ε$, estimates AUC within $ε/ 2$, and can maintain this estimate in $O((\log k) / ε)$ time, per update, as the window slides. This provides a speed-up over the exact computation of AUC, which requires $O(k)$ time, per update. The speed-up becomes more significant as the size of the window increases. Our estimate is based on grouping the data points together, and using these groups to calculate AUC. The grouping is designed carefully such that ($i$) the groups are small enough, so that the error stays small, ($ii$) the number of groups is small, so that enumerating them is not expensive, and ($iii$) the definition is flexible enough so that we can maintain the groups efficiently. Our experimental evaluation demonstrates that the average approximation error in practice is much smaller than the approximation guarantee $ε/ 2$, and that we can achieve significant speed-ups with only a modest sacrifice in accuracy.