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An Expected Utility Approach to Active Feature-Value Acquisition

9 years 5 months ago
An Expected Utility Approach to Active Feature-Value Acquisition
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 costeffective 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.
Prem Melville, Foster J. Provost, Raymond J. Moone
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICDM
Authors Prem Melville, Foster J. Provost, Raymond J. Mooney
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