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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2013

01-06-2013 | Original Article

Minimizing data consumption with sequential online feature selection

Authors: Thomas Rückstieß, Christian Osendorfer, Patrick van der Smagt

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2013

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Abstract

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.
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Footnotes
2
A partially observable MDP is a MDP with limited access to its states, i.e., the agent does not receive the full state information but only an incomplete observation based on the current state.
 
3
These costs represent a rough estimate of the time in minutes it takes to acquire the feature on a real patient. The estimates are based on oral communication with a local GP.
 
4
with the exception of the 5 rfa experiment, which only has 8 features in total. All of them carry information and an optimal static FS method would have to choose all 8.
 
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Metadata
Title
Minimizing data consumption with sequential online feature selection
Authors
Thomas Rückstieß
Christian Osendorfer
Patrick van der Smagt
Publication date
01-06-2013
Publisher
Springer-Verlag
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2013
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0092-x

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