Practical Uses of Belief Functions
It addresses data uncertainty issues for practitioners in fields like information retrieval and sensor networks, but is incremental as it reports on existing applications rather than introducing new methods.
The paper tackles problems with missing or messy data by applying belief functions, demonstrating sound and elegant solutions across four real-life examples, such as discriminant analysis with partially known classes and multi-sensor data combination.
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.