Minimizing Data Consumption with Sequential Online Feature Selection

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 algorith