Networks with Conditional Belief Functions Using the Monte Carlo Algorithm

Neverthe- less, a lot of the original data close to the middle of the clustering prototypes are considered as imprecise meta-cluster by ECM, which is unreasonable, and it is

Delaporte, Linear viscoelastic behavior of bituminous materials: from binders to mixes, 1 st EATA (European Asphalt Technology Association) Congress, Nottingham, Special Issue

The kernel density estimation was used to set mass functions, and the belief functions theory combined evidence to determine the sensor’s zone. Experiments on real data prove

The present paper focuses on both points by proposing a single association matrix, the dual pignistic matrix which gathers all the association information (targets ⇒ tracks and tracks

Abstract—The Evidential K-Nearest-Neighbor (EK-NN) method provided a global treatment of imperfect knowledge regarding the class membership of training patterns. It has

In this paper, belief functions, defined on the lattice of intervals partitions of a set of objects, are investigated as a suitable framework for combining mul- tiple clusterings.

(a) Strong dominance, maximality, e-admissibility (b) Weak dominance, interval-valued utility Figure 6: Iris data set: partial classification (partial preorder case) with the

• Belief degrees of consequence. For a complex classification problem, it is likely that the consequence of a rule may take a few values with different belief degrees. As indicated