Belief Functions: Theory and Applications: Proceedings of the 2nd International Conference on Belief Functions, Compiègne, France 9-11 May 2012

Front Matter....Pages 1-9
On Belief Functions and Random Sets....Pages 1-19
Evidential Multi-label Classification Using the Random k -Label Sets Approach....Pages 21-28
An Evidential Improvement for Gender Profiling....Pages 29-36
An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal Semantics....Pages 37-44
An Evidential Pattern Matching Approach for Vehicle Identification....Pages 45-52
A Comparison between a Bayesian Approach and a Method Based on Continuous Belief Functions for Pattern Recognition....Pages 53-60
Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief Functions....Pages 61-68
Adaptive Initialization of a EvKNN Classification Algorithm....Pages 69-76
Classification Trees Based on Belief Functions....Pages 77-84
Combination of Supervised and Unsupervised Classification Using the Theory of Belief Functions....Pages 85-92
Continuous Belief Functions: Focal Intervals Properties....Pages 93-100
Game-Theoretical Semantics of Epistemic Probability Transformations....Pages 101-108
Generalizations of the Relative Belief Transform....Pages 109-116
Choquet Integral as Maximum of Integrals with Respect to Belief Functions....Pages 117-124
Consonant Approximations in the Belief Space....Pages 125-133
Controling the Number of Focal Elements....Pages 135-143
Random Generation of Mass Functions: A Short Howto....Pages 145-152
Revisiting the Notion of Conflicting Belief Functions....Pages 153-160
About Conflict in the Theory of Belief Functions....Pages 161-168
The Internal Conflict of a Belief Function....Pages 169-177
Plausibility in DSmT....Pages 179-187
A Belief Function Model for Pixel Data....Pages 189-196
Using Belief Function Theory to Deal with Uncertainties and Imprecisions in Image Processing....Pages 197-204
Belief Theory for Large-Scale Multi-label Image Classification....Pages 205-212
Facial Expression Classification Based on Dempster-Shafer Theory of Evidence....Pages 213-220
Compositional Models in Valuation-Based Systems....Pages 221-228
Ascribing Causality from Interventional Belief Function Knowledge....Pages 229-237
About Sources Dependence in the Theory of Belief Functions....Pages 239-246
On Random Sets Independence and Strong Independence in Evidence Theory....Pages 247-254
Combining Linear Equation Models via Dempster’s Rule....Pages 255-265
Reliability in the Thresholded Dempster-Shafer Algorithm for ESM Data Fusion....Pages 267-274
Hierarchical Proportional Redistribution for bba Approximation....Pages 275-283
On the α -Conjunctions for Combining Belief Functions....Pages 285-292
Improvements to the GRP1 Combination Rule....Pages 293-300
Consensus-Based Credibility Estimation of Soft Evidence for Robust Data Fusion....Pages 301-309
Ranking from Pairwise Comparisons in the Belief Functions Framework....Pages 311-318
Dempster-Shafer Fusion of Context Sources for Pedestrian Recognition....Pages 319-326
Multi-level Dempster-Shafer Speed Limit Assistant....Pages 327-334
A New Local Measure of Disagreement between Belief Functions – Application to Localization....Pages 335-342
Map-Aided Fusion Using Evidential Grids for Mobile Perception in Urban Environment....Pages 343-350
Distributed Data Fusion for Detecting Sybil Attacks in VANETs....Pages 351-358
Partially-Hidden Markov Models....Pages 359-366
Large Scale Multinomial Inferences and Its Applications in Genome Wide Association Studies....Pages 367-374
Belief Function Robustness in Estimation....Pages 375-383
Conditioning in Dempster-Shafer Theory: Prediction vs. Revision....Pages 385-392
Combining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level Estimation....Pages 393-400
Sigmoidal Model for Belief Function-Based Electre Tri Method....Pages 401-408
Belief Inference with Timed Evidence....Pages 409-416
Evidential Network with Conditional Belief Functions for an Adaptive Training in Informed Virtual Environment....Pages 417-424
Using the Belief Functions Theory to Deploy Static Wireless Sensor Networks....Pages 425-432
A Quantitative Study of the Occurrence of a Railway Accident Based on Belief Functions....Pages 433-440
Back Matter....Pages 0--1