Confidence-Based Feature Acquisition to Minimize Training and Test Costs

We present Confidence-based Feature Acquisition (CFA), a novel supervised learning method for acquiring missing feature values when there is missing data at both training and test time. Previous work has considered the cases of missing data at training time (e.g., Active Feature Acquisition, AFA [8]), or at test time (e.g., Cost-Sensitive Naive Bayes, CSNB [2]), but not both. At training time, CFA constructs a cascaded ensemble of classifiers, starting with the zero-cost features and adding a single feature for each successive model. For each model, CFA selects a subset of training instances for which the added feature should be acquired. At test time, the set of models is applied sequentially (as a cascade), stopping when a user-supplied confidence threshold is met. We compare CFA to AFA, CSNB, and several other baselines, and find that CFA’s accuracy is at least as high as the other methods, while incurring significantly lower feature acquisition costs.