Belief Functions: Theory and Applications - Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France, 9-11 May 2012
نویسنده
چکیده
A basic belief assignment can have up to 2 focal elements, and combining them with a simple conjunctive operator will need O(2) operations. This article proposes some techniques to limit the size of the focal sets of the bbas to be combined while preserving a large part of the information they carry. The first section revisits some well-known definitions with an algorithmic point of vue. The second section proposes a matrix way of building the least committed isopignistic, and extends it to some other bodies of evidence. The third section adapts the k-means algorithm for an unsupervized clustering of the focal elements of a given bba.
منابع مشابه
belief function and the transferable belief model
Beliefs are the result of uncertainty. Sometimes uncertainty is because of a random process and sometimes the result of lack of information. In the past, the only solution in situations of uncertainty has been the probability theory. But the past few decades, various theories of other variables and systems are put forward for the systems with no adequate and accurate information. One of these a...
متن کاملFinite Alphabet Iterative Decoders, Part I: Decoding Beyond Belief Propagation on BSC
We introduce a new paradigm for finite precision iterative decoding on low-density parity-check codes over the Binary Symmetric channel. The messages take values from a finite alphabet, and unlike traditional quantized decoders which are quantized versions of the Belief propagation (BP) decoder, the proposed finite alphabet iterative decoders (FAIDs) do not propagate quantized probabilities or ...
متن کاملReply to the Comments of R. Haenni on the paper "Belief functions combination and conflict management
PSI, EA 2120, Université & INSA de Rouen Place Emile Blondel, F-76131 Mont Saint Aignan Cedex [email protected] IRCOM-SIC, UMR CNRS 6615, Bat. SP2MI BP 30179, Bd Marie et Pierre Curie, F-86962 Chasseneuil du Poitou Cedex [email protected] HEUDIASYC, UMR 6599 CNRS, Université de Technologie de Compiègne BP 20529, F-60205 Compiègne Cedex, France Patrick.Vannoorenberghe@hds...
متن کاملCombining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level Estimation
Estimation of extreme sea levels and waves for high return periods is of prime importance in hydrological design and flood risk assessment. The common practice consists of inferring design levels from the available observations and assuming the distribution of extreme values to be stationary. However, in the recent decades, more concern has been given to the integration of the effect of climate...
متن کاملCEVCLUS: evidential clustering with instance-level constraints for relational data
Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecisi...
متن کامل