An adaptive machine learning algorithm for the resource-constrained classification problem

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. We develop 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. The suggested approach reaches similar classification decisions for different costs, thus it may be useful when misclassification costs are not known precisely or are costly to achieve. We envision the suggested learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems.