Practical Uses of Belief Functions

Philippe Smets

100 citations0/0 files storedarXiv (Cornell University)

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Multi-Criteria Decision Making Bayesian Modeling and Causal Inference Fuzzy and Soft Set Theory

Abstract

We present examples where the use of belief functions provided sound and elegant solutions to real life problems. These are essentially characterized by ?missing' information. The examples deal with 1) discriminant analysis using a learning set where classes are only partially known; 2) an information retrieval systems handling inter-documents relationships; 3) the combination of data from sensors competent on partially overlapping frames; 4) the determination of the number of sources in a multi-sensor environment by studying the inter-sensors contradiction. The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle ?messy' data problems.

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References

1990The combination of evidence in the transferable belief model 1994Advances in the Dempster-Shafer theory of evidence 2008A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory 1999Uncertainty-Based Information 1990Constructing the Pignistic Probability Function in a Context of Uncertainty 1990Decision analysis using belief functions 1990The Dempster-Shafer theory of evidence 1995The canonical decomposition of a weighted belief

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