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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27807
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| Title: | Economical Active Feature-value Acquisition through Expected Utility Estimation |
| Authors: | Melville, Prem
Saar-Tsechansky, Maytal
Mooney, Raymond
Provost, Foster |
| Keywords: | machine learning
data mining
active learning
cost-sensitive learning |
| Issue Date: | Aug-2005 |
| Publisher: | IEEE Computer Society |
| Citation: | Proceedings of the KDD-05 Workshop on Utility-Based Data Mining, |
| Series/Report no.: | CeDER-PP-2005-05 |
| Abstract: | 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. |
| URI: | http://hdl.handle.net/2451/27807 |
| Appears in Collections: | CeDER Published Papers
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