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Active Feature Acquisition for Classifier Induction

Many induction problems, such as on-line customer profiling, include missing data that can be acquired at a cost, such as incomplete customer information that can be filled in by an intermediary. For building accurate predictive models, acquiring complete information for all instances is often prohibitively expensive or unnecessary. Randomly selecting instances for feature acquisition allows a representative sampling, but does not incorporate estimations of the value of acquisition. Active feature acquisition aims at reducing the cost of achieving a desired model accuracy by identifying instances for which complete information is most informative to obtain. We present approaches in which instances are selected for feature acquisition based on the current model’s ability to predict accurately and the model’s confidence in its prediction. Experimental results on several real-world data sets demonstrate that these approaches can induce accurate models using substantially fewer feature acquisitions, and suggest promising directions for improvements.