Economical Active Feature-value Acquisition through Expected Utility Estimation (2005)
P. Melville, M. Saar-Tsechansky, F. Provost and Raymond J. Mooney
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.
In Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, pp. 10-16, Chicago, IL, August 2005.

Prem Melville Ph.D. Alumni pmelvi [at] us ibm com
Raymond J. Mooney Faculty mooney [at] cs utexas edu