  Resource-constrained classification tasks are common in real-world
applications such as allocating tests for disease diagnosis, hiring decisions
when filling a limited number of positions, and defect detection in
manufacturing settings under a limited inspection budget. Typical
classification algorithms treat the learning process and the resource
constraints as two separate and sequential tasks. Here we design an adaptive
learning approach that considers resource constraints and learning jointly by
iteratively fine-tuning misclassification costs. Via a structured experimental
study using a publicly available data set, we evaluate a decision tree
classifier that utilizes the proposed approach. The adaptive learning approach
performs significantly better than alternative approaches, especially for
difficult classification problems in which the performance of common approaches
may be unsatisfactory. We envision the adaptive learning approach as an
important addition to the repertoire of techniques for handling
resource-constrained classification problems.
