Maintaining AUC and $H$-measure over time

TL;DR

This paper introduces efficient algorithms for maintaining AUC and H-measure in real-time as data points are added or removed, enabling faster performance monitoring of classifiers over time.

Contribution

It presents novel algorithms that efficiently update AUC and H-measure with dynamic data, improving over previous static computation methods.

Findings

Algorithms achieve O(log n) update time for AUC.

Convex hull maintenance enables efficient H-measure updates.

Empirical results show significant speed improvements over baselines.

Abstract

Measuring the performance of a classifier is a vital task in machine learning. The running time of an algorithm that computes the measure plays a very small role in an offline setting, for example, when the classifier is being developed by a researcher. However, the running time becomes more crucial if our goal is to monitor the performance of a classifier over time. In this paper we study three algorithms for maintaining two measures. The first algorithm maintains area under the ROC curve (AUC) under addition and deletion of data points in $O(\log n)$ time. This is done by maintaining the data points sorted in a self-balanced search tree. In addition, we augment the search tree that allows us to query the ROC coordinates of a data point in $O(\log n)$ time. In doing so we are able to maintain AUC in $O(\log n)$ time. Our next two algorithms involve in maintaining $H$-measure, an alternative measure based on the ROC curve. Computing the measure is a two-step process: first we need to compute a convex hull of the ROC curve, followed by a sum over the convex hull. We demonstrate that we can maintain the convex hull using a minor modification of the classic convex hull maintenance algorithm. We then show that under certain conditions, we can compute the $H$-measure exactly in $O(\log^2 n)$ time, and if the conditions are not met, then we can estimate the $H$-measure in $O((\log n + \epsilon^{-1})\log n)$ time. We show empirically that our methods are significantly faster than the baselines.