  In this paper, we propose an effective THresholding method based on ORder
Statistic, called THORS, to convert an arbitrary scoring-type classifier, which
can induce a continuous cumulative distribution function of the score, into a
cost-sensitive one. The procedure, uses order statistic to find an optimal
threshold for classification, requiring almost no knowledge of classifiers
itself. Unlike common data-driven methods, we analytically show that THORS has
theoretical guaranteed performance, theoretical bounds for the costs and lower
time complexity. Coupled with empirical results on several real-world data
sets, we argue that THORS is the preferred cost-sensitive technique.
