An Expected Utility Approach to Active Feature-Value Acquisition

  • Prem Melville
  • Raymond Mooney
  • Foster Provost
  • Maytal Saar-Tsechansky

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 an
approach that acquires feature values for inducing a classification model based on an estimation of the expected improvement in model accuracy per unit cost.  Experimental results demonstrate that our approach consistently reduces the cost of producing a model of a desired accuracy compared to random feature acquisitions.

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