  A central problem in business concerns the optimal allocation of limited
resources to a set of available tasks, where the payoff of these tasks is
inherently uncertain. In credit card fraud detection, for instance, a bank can
only assign a small subset of transactions to their fraud investigations team.
Typically, such problems are solved using a classification framework, where the
focus is on predicting task outcomes given a set of characteristics. Resources
are then allocated to the tasks that are predicted to be the most likely to
succeed. However, we argue that using classification to address task
uncertainty is inherently suboptimal as it does not take into account the
available capacity. Therefore, we first frame the problem as a type of
assignment problem. Then, we present a novel solution using learning to rank by
directly optimizing the assignment's expected profit given limited, stochastic
capacity. This is achieved by optimizing a specific instance of the net
discounted cumulative gain, a commonly used class of metrics in learning to
rank. Empirically, we demonstrate that our new method achieves higher expected
profit and expected precision compared to a classification approach for a wide
variety of application areas and data sets. This illustrates the benefit of an
integrated approach and of explicitly considering the available resources when
learning a predictive model.
