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Minimizing data consumption with sequential online feature selection
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DOI：10.1007/s13042-012-0092-x.png)
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Abstract
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In most real-world information processing problems, data is not a free resource. Its acquisition is often expensive and time-consuming. We investigate how such cost factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to reinforcement learning. Depending on previously selected features and the internal belief of the classifier, a next feature is chosen by a sequential online feature selection that learns which features are most informative at each time step. Experiments on toy datasets and a handwritten digits classification task show significant reduction in required data for correct classification, while a medical diabetes prediction task illustrates variable feature cost minimization as a further property of our algorithm.
Keywords:
Reinforcement learning
Feature selection
Classification
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