Partially supervised learning by a credal EM approach

Authors:
Patrick Vannoorenberghe;Philippe Smets
Affiliations:
PSI, FRE 2645 CNRS, Université de Rouen, Mont Saint Aignan cedex, France;IRIDIA, Université Libre de Bruxelles, Bruxelles, Belgique
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

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Abstract

In this paper, we propose a Credal EM (CrEM) approach for partially supervised learning. The uncertainty is represented by belief functions as understood in the transferable belief model (TBM). This model relies on a non probabilistic formalism for representing and manipulating imprecise and uncertain information. We show how the EM algorithm can be applied within the TBM framework when applied for the classification of objects and when the learning set is imprecise (the actual class of each object is only known as belonging to a subset of classes), and/or uncertain (the knowledge about the actual class is represented by a probability function or by a belief function).