Abstract
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.
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Jraidi, I., Elouedi, Z. (2007). Belief Classification Approach Based on Generalized Credal EM. In: Mellouli, K. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2007. Lecture Notes in Computer Science(), vol 4724. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75256-1_47
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DOI: https://doi.org/10.1007/978-3-540-75256-1_47
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