Fast margin-based cost-sensitive classification

We present a novel classification algorithm for learning with test time budgets. In this setting, the goal is to reduce feature acquisition cost while maintaining classification accuracy. For every decision, our approach dynamically selects features based on previously observed information. Once a desired confidence of a decision is achieved, the acquisition stops and the test instance is classified. Our approach can be used in conjunction with many popular margin based classification algorithms. We use margin information from training data in the partial feature neighborhood of a test point to compute a probability of correct classification. This estimate is used to either select the next feature or to stop. We compare our algorithm to other cost-sensitive methods on real world datasets. The experiments demonstrate that our algorithm provides an accurate estimate of classification confidence and outperforms other approaches while being significantly more efficient in computation.