Classification of emotional states in a woz scenario exploiting labeled and unlabeled bio-physiological data

Authors:
Martin Schels;Markus Kächele;David Hrabal;Steffen Walter;Harald C. Traue;Friedhelm Schwenker
Affiliations:
Institute of Neural Information Processing, University of Ulm, Germany;Institute of Neural Information Processing, University of Ulm, Germany;Medical Psychology, University of Ulm, Germany;Medical Psychology, University of Ulm, Germany;Medical Psychology, University of Ulm, Germany;Institute of Neural Information Processing, University of Ulm, Germany
Venue:
PSL'11 Proceedings of the First IAPR TC3 conference on Partially Supervised Learning
Year:
2011

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Abstract

In this paper, a partially supervised machine learning approach is proposed for the recognition of emotional user states in HCI from bio-physiological data. To do so, an unsupervised learning preprocessing step is integrated into the training of a classifier. This makes it feasible to utilize unlabeled data or --- as it is conducted in this study --- data that is labeled in others than the considered categories. Thus, the data is transformed into a new representation and a standard classifier approach is subsequently applied. Experimental evidences that such an approach is beneficial in this particular setting is provided using classification experiments. Finally, the results are discussed and arguments when such an partially supervised approach is promising to yield robust and increased classification performances are given.