Representation, independence, and combination of evidence in the Dempster-Shafer theory
Advances in the Dempster-Shafer theory of evidence
Fast Algorithms for Dempster-Shafer Theory
IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Multimodal emotion classification in naturalistic user behavior
HCII'11 Proceedings of the 14th international conference on Human-computer interaction: towards mobile and intelligent interaction environments - Volume Part III
Penalty logic and its link with Dempster-Shafer theory
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Vowels formants analysis allows straightforward detection of high arousal emotions
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Fusion of fragmentary classifier decisions for affective state recognition
MPRSS'12 Proceedings of the First international conference on Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction
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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.