The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering approach using belief function theory
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Partially supervised learning by a credal EM approach
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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The EM algorithm is widely used in supervised and unsupervised classification when applied for mixture model parameter estimation. It has been shown that this method can be applied for partially supervised classification where the knowledge about the class labels of the observations can be imprecise and/or uncertain. In this paper, we propose to generalize this approach to cope with imperfect knowledge at two levels: the attribute values of the observations and their class labels. This knowledge is represented by belief functions as understood in the Transferable Belief Model. We show that this approach can be applied when the data are categorical and generated from multinomial mixtures.