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International Conference on Affective Computing and Intelligent Interaction

ACII 2011: Affective Computing and Intelligent Interaction pp 132–141Cite as

Active Class Selection for Arousal Classification

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6975))

Abstract

Active class selection (ACS) studies how to optimally select the classes to obtain training examples so that a good classifier can be constructed from a small number of training examples. It is very useful in situations where the class labels need to be determined before the training examples and features can be obtained. For example, in many emotion classification problems, the emotion (class label) needs to be specified before the corresponding responses can be generated and recorded. However, there has been very limited research on ACS, and to the best knowledge of the authors, ACS has not been introduced to the affective computing community. In this paper, we compare two ACS approaches in an arousal classification application. Experimental results using a kNN classifier show that one of them almost always results in higher classification accuracy than a uniform sampling approach. We expect that ACS, together with transfer learning, will greatly reduce the data acquisition effort to customize an affective computing system.

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Author information

Authors and Affiliations

  1. Machine Learning Laboratory, GE Global Research Center One Research Circle, Niskayuna, NY, 12309, USA

    Dongrui Wu

  2. Institute for Creative Technologies, University of Southern California, 12015 Waterfront Drive, Playa Vista, CA, 90094, USA

    Thomas D. Parsons

Authors
  1. Dongrui Wu
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  2. Thomas D. Parsons
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Editor information

Editors and Affiliations

  1. University of Memphis, 202 Psychology Building, 38152, Memphis, TN, USA

    Sidney D’Mello  & Arthur Graesser  & 

  2. Technische Universität München, Arcisstraße 21, 80333, München, Germany

    Björn Schuller

  3. Laboratoire d’Informatique pour la Mécanique et les Sciences de l’Ingénieur (LIMSI-CNRS), Bâtiment 508, 91403, Orsay Cedex, France

    Jean-Claude Martin

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© 2011 Springer-Verlag Berlin Heidelberg

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Wu, D., Parsons, T.D. (2011). Active Class Selection for Arousal Classification. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_14

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  • DOI: https://doi.org/10.1007/978-3-642-24571-8_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24570-1

  • Online ISBN: 978-3-642-24571-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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