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 |
Files in This Item:
File | Description | Size | Format | |
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CPP-05-05.pdf | 103.47 kB | Adobe PDF | View/Open |
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