Classifier Construction Under Budget Constraints

Search mechanisms over large assortments of items are central to the operation of many platforms. As users commonly express filtering conditions based on item properties that are not initially stored, companies must derive the missing information by training and applying binary classifiers. Choosing which classifiers to construct is however not trivial, since classifiers differ in construction costs and range of applicability. Previous work has considered the problem of selecting a classifier set of minimum construction cost, but this has been done under the (often unrealistic) assumption that the available budget is unlimited and allows to support all search queries. In practice, budget constraints require prioritizing some queries over others. To capture this consideration, we study in this work a more general model that allows assigning to each search query a score that models how important it is to compute its result set and examine the optimization problem of selecting a classifier set, whose cost is within the budget, that maximizes the overall score of the queries it can answer.

We show that this generalization is likely much harder to approximate complexity-wise, even assuming limited special cases. Nevertheless, we devise a heuristic algorithm, whose effectiveness is demonstrated in our experimental study over real-world data, consisting of a public dataset and datasets provided by a large e-commerce company that include costs and scores derived by business analysts. Finally, we show that our methods are applicable also for related problems in practical settings where there is some flexibility in determining the budget.