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A central problem in business concerns the optimal allocation of limitedresources to a set of available tasks, where the payoff of these tasks isinherently uncertain. In credit card fraud detection, for instance, a bank canonly assign a small subset of transactions to their fraud investigations team.Typically, such problems are solved using a classification framework, where thefocus is on predicting task outcomes given a set of characteristics. Resourcesare then allocated to the tasks that are predicted to be the most likely tosucceed. However, we argue that using classification to address taskuncertainty is inherently suboptimal as it does not take into account theavailable capacity. Therefore, we first frame the problem as a type ofassignment problem. Then, we present a novel solution using learning to rank bydirectly optimizing the assignment's expected profit given limited, stochasticcapacity. This is achieved by optimizing a specific instance of the netdiscounted cumulative gain, a commonly used class of metrics in learning torank. Empirically, we demonstrate that our new method achieves higher expectedprofit and expected precision compared to a classification approach for a widevariety of application areas and data sets. This illustrates the benefit of anintegrated approach and of explicitly considering the available resources whenlearning a predictive model.

A new perspective on classification: optimally allocating limited resources to uncertain tasks