  We study the behavior of linear discriminant functions for binary
classification in the infinite-imbalance limit, where the sample size of one
class grows without bound while the sample size of the other remains fixed. The
coefficients of the classifier minimize an empirical loss specified through a
weight function. We show that for a broad class of weight functions, the
intercept diverges but the rest of the coefficient vector has a finite almost
sure limit under infinite imbalance, extending prior work on logistic
regression. The limit depends on the left-tail growth rate of the weight
function, for which we distinguish two cases: subexponential and exponential.
The limiting coefficient vectors reflect robustness or conservatism properties
in the sense that they optimize against certain worst-case alternatives. In the
subexponential case, the limit is equivalent to an implicit choice of
upsampling distribution for the minority class. We apply these ideas in a
credit risk setting, with particular emphasis on performance in the
high-sensitivity and high-specificity regions.
