Table of Contents

• On Belief Functions and Random SetsHung T. Nguyen. 1-19 [doi]
• Evidential Multi-label Classification Using the Random k-Label Sets ApproachSawsan Kanj, Fahed Abdallah, Thierry Denoeux. 21-28 [doi]
• An Evidential Improvement for Gender ProfilingJianbing Ma, Weiru Liu, Paul Miller. 29-36 [doi]
• An Interval-Valued Dissimilarity Measure for Belief Functions Based on Credal SemanticsAlessandro Antonucci. 37-44 [doi]
• An Evidential Pattern Matching Approach for Vehicle IdentificationAnne-Laure Jousselme, Patrick Maupin. 45-52 [doi]
• A Comparison between a Bayesian Approach and a Method Based on Continuous Belief Functions for Pattern RecognitionAnthony Fiche, Arnaud Martin, Jean-Christophe Cexus, Ali Khenchaf. 53-60 [doi]
• Prognostic by Classification of Predictions Combining Similarity-Based Estimation and Belief FunctionsEmmanuel Ramasso, Michèle Rombaut, Noureddine Zerhouni. 61-68 [doi]
• Adaptive Initialization of a EvKNN Classification AlgorithmStefen Chan Wai Tim, Michèle Rombaut, Denis Pellerin. 69-76 [doi]
• Classification Trees Based on Belief FunctionsNicolas Sutton-Charani, Sébastien Destercke, Thierry Denoeux. 77-84 [doi]
• Combination of Supervised and Unsupervised Classification Using the Theory of Belief FunctionsFatma Karem, Mounir Dhibi, Arnaud Martin. 85-92 [doi]
• Continuous Belief Functions: Focal Intervals PropertiesJean-Marc Vannobel. 93-100 [doi]
• Game-Theoretical Semantics of Epistemic Probability TransformationsFabio Cuzzolin. 101-108 [doi]
• Generalizations of the Relative Belief TransformFabio Cuzzolin. 109-116 [doi]
• Choquet Integral as Maximum of Integrals with Respect to Belief FunctionsMikhail Timonin. 117-124 [doi]
• Consonant Approximations in the Belief SpaceFabio Cuzzolin. 125-133 [doi]
• Controling the Number of Focal Elements - Some Combinatorial ConsiderationsChristophe Osswald. 135-143 [doi]
• Random Generation of Mass Functions: A Short HowtoThomas Burger, Sébastien Destercke. 145-152 [doi]
• Revisiting the Notion of Conflicting Belief FunctionsSébastien Destercke, Thomas Burger. 153-160 [doi]
• About Conflict in the Theory of Belief FunctionsArnaud Martin. 161-168 [doi]
• The Internal Conflict of a Belief FunctionJohan Schubert. 169-177 [doi]
• Plausibility in DSmTMilan Daniel. 179-187 [doi]
• A Belief Function Model for Pixel DataJohn Klein, Olivier Colot. 189-196 [doi]
• Using Belief Function Theory to Deal with Uncertainties and Imprecisions in Image ProcessingBenoît Lelandais, Isabelle Gardin, Laurent Mouchard, Pierre Vera, Su Ruan. 197-204 [doi]
• Belief Theory for Large-Scale Multi-label Image ClassificationAmel Znaidia, Hervé Le Borgne, Céline Hudelot. 205-212 [doi]
• Facial Expression Classification Based on Dempster-Shafer Theory of EvidenceMohammad Shoyaib, Mohammad Abdullah-Al-Wadud, S. M. Zahid Ishraque, Oksam Chae. 213-220 [doi]
• Compositional Models in Valuation-Based SystemsRadim Jirousek, Prakash P. Shenoy. 221-228 [doi]
• Ascribing Causality from Interventional Belief Function KnowledgeImen Boukhris, Salem Benferhat, Zied Elouedi. 229-237 [doi]
• About Sources Dependence in the Theory of Belief FunctionsMouna Chebbah, Arnaud Martin, Boutheina Ben Yaghlane. 239-246 [doi]
• On Random Sets Independence and Strong Independence in Evidence TheoryJirina Vejnarová. 247-254 [doi]
• Combining Linear Equation Models via Dempster's RuleLiping Liu. 255-265 [doi]
• Reliability in the Thresholded Dempster-Shafer Algorithm for ESM Data FusionMelita Hadzagic, Marie-Odette St-Hilaire, Pierre Valin. 267-274 [doi]
• Hierarchical Proportional Redistribution for bba ApproximationJean Dezert, Deqiang Han, Zhunga Liu, Jean-Marc Tacnet. 275-283 [doi]
• On the α-Conjunctions for Combining Belief FunctionsFrédéric Pichon. 285-292 [doi]
• Improvements to the GRP1 Combination RuleGavin Powell, Matthew Roberts, Dafni Stampouli. 293-300 [doi]
• Consensus-Based Credibility Estimation of Soft Evidence for Robust Data FusionThanuka Wickramarathne, Kamal Premaratne, Manohar N. Murthi. 301-309 [doi]
• Ranking from Pairwise Comparisons in the Belief Functions FrameworkMarie-Hélène Masson, Thierry Denoeux. 311-318 [doi]
• Dempster-Shafer Fusion of Context Sources for Pedestrian RecognitionMagdalena Szczot, Otto Löhlein, Günther Palm. 319-326 [doi]
• Multi-level Dempster-Shafer Speed Limit AssistantJérémie Daniel, Jean-Philippe Lauffenburger. 327-334 [doi]
• A New Local Measure of Disagreement between Belief Functions - Application to LocalizationArnaud Roquel, Sylvie Le Hégarat-Mascle, Isabelle Bloch, Bastien Vincke. 335-342 [doi]
• Map-Aided Fusion Using Evidential Grids for Mobile Perception in Urban EnvironmentMarek Kurdej, Julien Moras, Véronique Cherfaoui, Philippe Bonnifait. 343-350 [doi]
• Distributed Data Fusion for Detecting Sybil Attacks in VANETsNicole El Zoghby, Véronique Cherfaoui, Bertrand Ducourthial, Thierry Denoeux. 351-358 [doi]
• Partially-Hidden Markov ModelsEmmanuel Ramasso, Thierry Denoeux, Noureddine Zerhouni. 359-366 [doi]
• Large Scale Multinomial Inferences and Its Applications in Genome Wide Association StudiesChuanhai Liu, Jun Xie. 367-374 [doi]
• Belief Function Robustness in EstimationAlessio Benavoli. 375-383 [doi]
• Conditioning in Dempster-Shafer Theory: Prediction vs. RevisionDidier Dubois, Thierry Denoeux. 385-392 [doi]
• Combining Statistical and Expert Evidence within the D-S Framework: Application to Hydrological Return Level EstimationNadia Ben Abdallah, Nassima Mouhous Voyneau, Thierry Denoeux. 393-400 [doi]
• Sigmoidal Model for Belief Function-Based Electre Tri MethodJean Dezert, Jean-Marc Tacnet. 401-408 [doi]
• Belief Inference with Timed Evidence - Methodology and Application Using Sensors in a Smart HomeBastien Pietropaoli, Michele Dominici, Frédéric Weis. 409-416 [doi]
• Evidential Network with Conditional Belief Functions for an Adaptive Training in Informed Virtual EnvironmentLoïc Fricoteaux, Indira Thouvenin, Jérôme Olive, Paul George. 417-424 [doi]
• Using the Belief Functions Theory to Deploy Static Wireless Sensor NetworksMustapha Réda Senouci, Abdelhamid Mellouk, Latifa Oukhellou, Amar Aissani. 425-432 [doi]
• A Quantitative Study of the Occurrence of a Railway Accident Based on Belief FunctionsFelipe Aguirre, Mohamed Sallak, Walter Schön, Fabien Belmonte. 433-440 [doi]