Theory of Belief Functions: Application to Classification and Clustering
Speaker
Abstract
| The theory of belief function (also referred to as Dempster-Shafer theory) is a generalization of probability theory allowing for the representation of uncertain and imprecise knowledge. Introduced in the context of statistical inference by A. P. Dempster in the 1960’s, it was developed by G. Shafer in the 1970’s as a general framework for combining evidence and reasoning under uncertainty. After a general introduction to this theory, we will focus on its application to data classification and clustering. As will be shown, Dempster-Shafer theory makes it possible to handle uncertain and imprecise observations, such as partially supervised data in classification tasks. The language of belief functions also allows us to generate rich descriptions of the data (using, e.g., the new concept of credal partition in clustering problems), and to combine efficiently information coming from several sources (such as statistical data and expert knowledge). |
| Contact information: |
| Erik Kole |
