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Sequential Feature Selection for Classification

  • Thomas Rückstieß
  • Christian Osendorfer
  • Patrick van der Smagt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7106)

Abstract

In most real-world information processing problems, data is not a free resource; its acquisition is rather time-consuming and/or expensive. We investigate how these two factors can be included in supervised classification tasks by deriving classification as a sequential decision process and making it accessible to Reinforcement Learning. Our method performs a sequential feature selection that learns which features are most informative at each timestep, choosing the next feature depending on the already selected features and the internal belief of the classifier. Experiments on 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Thomas Rückstieß
    • 1
  • Christian Osendorfer
    • 1
  • Patrick van der Smagt
    • 2
  1. 1.Technische Universität MünchenGarchingGermany
  2. 2.German Aerospace Center / DLRWesslingGermany

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