  In many data classification problems, there is no linear relationship between
an explanatory and the dependent variables. Instead, there may be ranges of the
input variable for which the observed outcome is signficantly more or less
likely. This paper describes an algorithm for automatic detection of such
thresholds, called the Univariate Flagging Algorithm (UFA). The algorithm
searches for a separation that optimizes the difference between separated areas
while providing the maximum support. We evaluate its performance using three
examples and demonstrate that thresholds identified by the algorithm align well
with visual inspection and subject matter expertise. We also introduce two
classification approaches that use UFA and show that the performance attained
on unseen test data is equal to or better than that of more traditional
classifiers. We demonstrate that the proposed algorithm is robust against
missing data and noise, is scalable, and is easy to interpret and visualize. It
is also well suited for problems where incidence of the target is low.
