In many classification tasks training data have missing feature values that can be acquired at a cost. For building accurate predictive models, acquiring all missing values is often prohibitively expensive or unnecessary, while acquiring a random subset of feature values may not be most effective. The goal of Active Feature-value Acquisition is to incrementally select feature values that are most cost-effective for improving the model's accuracy. We present two policies, Sampled Expected Utility and Expected Utility-ES, that acquire feature values for inducing a classification model based on an estimation of the expected improvement in model accuracy per unit cost. A comparison of the two policies to each other and to alternative policies demonstrate that Sampled Expected Utility is preferable as it effectively reduces the cost of producing a model of a desired accuracy and exhibits a consistent performance across domains.

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In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, pp. 10-16, Chicago, IL, August 2005.

@InProceedings{melville:kdd-wkshp05, title={Economical Active Feature-value Acquisition through Expected Utility Estimation}, author={P. Melville and M. Saar-Tsechansky and F. Provost and Raymond J. Mooney}, booktitle={Proceedings of the KDD-05 Workshop on Utility-Based Data Mining}, month={August}, address={Chicago, IL}, pages={10-16}, url="http://www.cs.utexas.edu/users/ai-lab?melville:kdd-wkshp05", year={2005} }