A neural model for fuzzy Dempster–Shafer classifiers Original Research Article

This paper presents a supervised classification model integrating fuzzy reasoning and Dempster–Shafer propagation of evidence has been built on top of connectionist techniques to address classification tasks in which vagueness and ambiguity coexist. The salient aspect of the approach is the integration within a neuro-fuzzy system of knowledge structures and inferences for evidential reasoning based on Dempster–Shafer theory. In this context the learning task can be formulated as the search for the most adequate “ingredients” of the fuzzy and Dempster–Shafer frameworks such as the fuzzy aggregation operators, for fusing data from different sources and focal elements, and basic probability assignments, describing the contributions of evidence in the inference scheme. The new neural model allows us to establish a complete correspondence between connectionist elements and fuzzy and Dempster–Shafer ingredients, ensuring both a high level of interpretability, and transparency and high performance in classification. Experiments with simulated data show that the network can cope well with problems of different complexity. The experiments with real data show the superiority of the neural implementation with respect to the symbolic representation, and prove that the integration of the propagation of evidence provides better classification results and fuzzy reasoning within connectionist schema than those obtained by pure neuro-fuzzy models.