Economical Active Feature-value Acquisition through Expected Utility Estimation
|Keywords:||machine learning;data mining;active learning;cost-sensitive learning|
|Publisher:||IEEE Computer Society|
|Citation:||Proceedings of the KDD-05 Workshop on Utility-Based Data Mining,|
|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.|
|Appears in Collections:||CeDER Published Papers|
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