Using information criteria in Dempster-Shafer´s basic belief assignment

Within the framework of pattern recognition, many methods of classification were developed. More recently, techniques using the Dempster-Shafer´s theory or evidence theory tried to deal with the problem related to the management of the uncertainty and data fusion. We propose a classification method based on Dempster-Shafer´s theory and information criteria. After an original basic belief assignment method, we introduce an attenuation factor of belief functions based on the dissimilarity between probability distributions. Results on synthetic and real world data sets are given in order to illustrate the proposed methodology.