Abstract
This paper introduces the idea of a modified Dempster-Shafer theory. We adapt the belief characteristic of expert combination by introducing a penalty term which is specific to the investigated object. This approach is motivated by the observation that final decisions in the Dempster-Shafer theory might tend to fluctuations due to variations in sensor inputs on small time scales, even if the real phenomenological characteristic is stable.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Böck, R., Limbrecht, K., Siegert, I., Glüge, S., Walter, S., Wendemuth, A.: Combining mimic and prosodic analyses for user disposition classification. In: Proceedings of the 23. Konferenz Elektronische Sprachsignalverarbeitung (ESSV 2012), pp. 220–228 (2012)
Böck, R., Limbrecht, K., Walter, S., Hrabal, D., Traue, H.C., Glüge, S., Wendemuth, A.: Intraindividual and interindividual multimodal emotion analyses in human-machine-interaction. In: 2012 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), pp. 59–64 (2012)
Dempster, A.P.: A generalization of bayesian inference. In: Yager, R.R., Liu, L. (eds.) Classic Works of the Dempster-Shafer Theory of Belief Functions. STUDFUZZ, vol. 219, pp. 73–104. Springer, Heidelberg (2008); reprint of a talk given at a Research Methods Meeting of the Royal Statistical Society (February 14, 1968)
Kennes, R., Smets, P.: Fast algorithms for Dempster-Shafer theory. In: Bouchon-Meunier, B., Zadeh, L.A., Yager, R.R. (eds.) IPMU 1990. LNCS, vol. 521, pp. 14–23. Springer, Heidelberg (1991)
Koelstra, S., Mühl, C., Patras, I.: Eeg analysis for implicit tagging of video data. In: 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, pp. 1–6. IEEE (2009)
Krell, G., Glodek, M., Panning, A., Siegert, I., Michaelis, B., Wendemuth, A., Schwenker, F.: Fusion of fragmentary classifier decisions for affective state recognition. In: Proceedings of the First IAPR Workshop on Multimodal Pattern Recognition of Social Signals in Human Computer Interaction, Tsukuba Science City, Japan (2012) (to appear)
Dupin de Saint-Cyr, F., Lang, J., Sabatier, P., Schiex, T.: Penalty logic and its link with dempster-shafer theory. In: Proc. of the 10th Conf. on Uncertainty in Artificial Intelligence, pp. 204–211. Morgan Kaufmann (1994)
Scherer, K.R.: What are emotions? and how can they be measured? Social Science Information 44(4), 695–729 (2005)
Sentz, K., Ferson, S.: Combination of evidence in dempster-shafer theory. Tech. rep., Sandia National Laboratories (2002)
Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press (1976)
Vlasenko, B., Böck, R., Wendemuth, A.: Modeling affected user behavior during human-machine interaction. In: Proceedings of the 5th International Conference on Speech Prosody 2010, Chicago, Illinois, USA, pp. 44–47 (2010)
Vlasenko, B., Philippou-Hübner, D., Prylipko, D., Böck, R., Siegert, I., Wendemuth, A.: Vowels formants analysis allows straightforward detection of high arousal emotions. In: Proceedings of the IEEE International Conference on Multimedia and Expo, ICME 2011, Barcelona, Spain (2011), elec. resource
Walter, S., et al.: Multimodal Emotion Classification in Naturalistic User Behavior. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part III, HCII 2011. LNCS, vol. 6763, pp. 603–611. Springer, Heidelberg (2011)
Zhang, L.: Representation, independence, and combination of evidence in the dempster-shafer theory. In: Yager, R.R., Fedrizzi, M., Kacprzyk, J. (eds.) Advances in the Dempster-Shafer Theory of Evidence, pp. 51–69. Wiley (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Böck, R., Glüge, S., Wendemuth, A. (2013). Dempster-Shafer Theory with Smoothness. In: Qin, Z., Huynh, VN. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2013. Lecture Notes in Computer Science(), vol 8032. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39515-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-39515-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39514-7
Online ISBN: 978-3-642-39515-4
eBook Packages: Computer ScienceComputer Science (R0)