  The problem of active diagnosis arises in several applications such as
disease diagnosis, and fault diagnosis in computer networks, where the goal is
to rapidly identify the binary states of a set of objects (e.g., faulty or
working) by sequentially selecting, and observing, (noisy) responses to binary
valued queries. Current algorithms in this area rely on loopy belief
propagation for active query selection. These algorithms have an exponential
time complexity, making them slow and even intractable in large networks. We
propose a rank-based greedy algorithm that sequentially chooses queries such
that the area under the ROC curve of the rank-based output is maximized. The
AUC criterion allows us to make a simplifying assumption that significantly
reduces the complexity of active query selection (from exponential to near
quadratic), with little or no compromise on the performance quality.
