  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.
