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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27801
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| Title: | Active Feature-Value Acquisition for Classifier Induction |
| Authors: | Melville, Prem
Saar-Tsechansky, Maytal
Provost, Foster
Mooney, Raymond |
| Issue Date: | Nov-2004 |
| Publisher: | Proceedings of the 4th IEEE International Conference on Data Mining, |
| Citation: | Proceedings of the 4th IEEE International Conference on Data Mining, |
| Series/Report no.: | CeDER-PP-2004-06 |
| Abstract: | Many induction problems include missing data that can be acquired at a
cost. For building accurate predictive models, acquiring complete
information for all instances is often expensive or unnecessary, while
acquiring information for a random subset of instances may not be most
effective. Active feature-value acquisition tries to reduce the cost of
achieving a desired model accuracy by identifying instances for which
obtaining complete information is most informative. We present an
approach in which instances are selected for acquisition based on the
current model’s accuracy and its confidence in the prediction.
Experimental results demonstrate that our approach can induce accurate
models using substantially fewer feature-value acquisitions as compared
to alternative policies. |
| URI: | http://hdl.handle.net/2451/27801 |
| Appears in Collections: | CeDER Published Papers
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