L. Abassi and I. Boukhris, Crowd Label Aggregation Under a Belief Function Framework, Knowledge Science, Engineering and Management: 9th International Conference, KSEM 2016 Proceedings, pp.185-196, 2016.
DOI : 10.3115/1613715.1613751

B. Rjab, A. Kharoune, M. Miklos, Z. Martin, and A. , Characterization of experts in crowdsourcing platforms, Belief Functions: Theory and Applications: 4th International Conference Proceedings, pp.97-104, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01372142

, Pattern Recognition and Machine Learning, Bishop CM, 2006.

O. Chapelle, B. Schölkopf, and A. Zien, Semi-Supervised Learning, 2006.
DOI : 10.7551/mitpress/9780262033589.001.0001

Z. Cherfi, L. Oukhellou, E. Côme, T. Denoeux, and P. Aknin, Partially supervised Independent Factor Analysis using soft labels elicited from multiple experts: application to railway track circuit diagnosis, Soft Computing, vol.41, issue.2, pp.741-754, 2012.
DOI : 10.1016/0020-0255(87)90007-7

URL : https://hal.archives-ouvertes.fr/hal-00688783

E. Côme, L. Oukhellou, T. Denoeux, and P. Aknin, Learning from partially supervised data using mixture models and belief functions, Pattern Recognition, vol.42, issue.3, pp.334-348, 2009.
DOI : 10.1016/j.patcog.2008.07.014

T. Cour, B. Sapp, and B. Taskar, Learning from partial labels, Journal of Machine Learning Research, vol.12, pp.1225-1261, 2011.

I. Couso, D. Dubois, M. Ferraro, P. Giordani, B. Vantaggi et al., Maximum Likelihood Under Incomplete Information: Toward a Comparison of Criteria, Soft Methods for Data Science, pp.141-148, 2017.
DOI : 10.1016/j.ijar.2004.05.003

A. Dempster, Upper and Lower Probabilities Induced by a Multivalued Mapping, The Annals of Mathematical Statistics, vol.38, issue.2, pp.325-339, 1967.
DOI : 10.1214/aoms/1177698950

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society B, vol.39, pp.1-38, 1977.

T. Denoeux, A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Transactions on Systems, Man, and Cybernetics, vol.25, issue.5, pp.804-813, 1995.
DOI : 10.1109/21.376493

T. Denoeux, Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.1, pp.119-130, 2013.
DOI : 10.1109/TKDE.2011.201

URL : https://hal.archives-ouvertes.fr/hal-00804343

T. Denoeux, Likelihood-based belief function: Justification and some extensions to low-quality data, International Journal of Approximate Reasoning, vol.55, issue.7, pp.1535-1547, 2014.
DOI : 10.1016/j.ijar.2013.06.007

URL : https://hal.archives-ouvertes.fr/hal-00813021

T. Denoeux and O. Kanjanatarakul, Beyond fuzzy, possibilistic and rough: An investigation of belief functions in clustering In: Soft Methods for Data Science, Proc. of the 8th International Conference on Soft Methods in Probability and Statistics SMPS 2016) Advances in Intelligent and Soft Computing, pp.157-164, 2016.

T. Denoeux and M. Masson, EVCLUS: Evidential Clustering of Proximity Data, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.95-109, 2004.
DOI : 10.1109/TSMCB.2002.806496

T. Denoeux and M. Skarstein-bjanger, Induction of decision trees for partially classified data, Proceedings of SMC'2000, pp.2923-2928, 2000.

T. Denoeux and L. Zouhal, Handling possibilistic labels in pattern classification using evidential reasoning, Fuzzy Sets and Systems, vol.122, issue.3, pp.47-62, 2001.
DOI : 10.1016/S0165-0114(00)00086-5

T. Denoeux, S. Sriboonchitta, and O. Kanjanatarakul, Evidential clustering of large dissimilarity data, Knowledge-Based Systems, vol.106, pp.179-195, 2016.
DOI : 10.1016/j.knosys.2016.05.043

URL : https://hal.archives-ouvertes.fr/hal-01324491

S. Dubuisson, F. Davoine, and M. Masson, A solution for facial expression representation and recognition, Signal Processing: Image Communication, vol.17, issue.9, pp.657-673, 2002.
DOI : 10.1016/S0923-5965(02)00076-0

URL : https://hal.archives-ouvertes.fr/hal-00143701

Z. Elouedi, K. Mellouli, and P. Smets, Belief decision trees: theoretical foundations, International Journal of Approximate Reasoning, vol.28, issue.2-3, pp.91-124, 2001.
DOI : 10.1016/S0888-613X(01)00045-7

A. Hasan, Z. Wang, and A. Mahani, Journal of Statistical Software, vol.75, issue.3, pp.1-24, 2016.
DOI : 10.18637/jss.v075.i03

D. Heitjan and D. Rubin, Ignorability and Coarse Data, The Annals of Statistics, vol.19, issue.4, pp.2244-2253, 1991.
DOI : 10.1214/aos/1176348396

E. Hüllermeier, Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization, International Journal of Approximate Reasoning, vol.55, issue.7, pp.1519-1534, 2014.
DOI : 10.1016/j.ijar.2013.09.003

E. Hüllermeier and J. Beringer, Learning from ambiguously labeled examples, Proceedings of the 6th International Symposium on Intelligent Data Analysis (IDA-05), 2005.

J. Jaffray, Linear utility theory for belief functions, Operations Research Letters, vol.8, issue.2, pp.107-112, 1989.
DOI : 10.1016/0167-6377(89)90010-2

T. Kanade, J. Cohn, and Y. Tian, Comprehensive database for facial expression analysis, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580), pp.46-53, 2000.
DOI : 10.1109/AFGR.2000.840611

J. Li, Logistic Regression, Course Notes, 2013.
DOI : 10.1007/978-1-4419-9863-7_396

URL : https://hal.archives-ouvertes.fr/hal-01394868

Z. Liu, Q. Pan, J. Dezert, and G. Mercier, Credal c-means clustering method based on belief functions, Knowledge-Based Systems, vol.74, issue.0, pp.119-132, 2015.
DOI : 10.1016/j.knosys.2014.11.013

Z. Liu, Q. Pan, J. Dezert, and G. Mercier, Hybrid Classification System for Uncertain Data, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.47, issue.10, p.2622247, 2017.
DOI : 10.1109/TSMC.2016.2622247

URL : https://hal.archives-ouvertes.fr/hal-01848997

L. Ma, S. Destercke, and Y. Wang, Online active learning of decision trees with evidential data, Pattern Recognition, vol.52, pp.33-45, 2016.
DOI : 10.1016/j.patcog.2015.10.014

URL : https://hal.archives-ouvertes.fr/hal-01254290

K. Mardia, Measures of multivariate skewness and kurtosis with applications, Biometrika, vol.57, issue.3, pp.519-530, 1970.
DOI : 10.1093/biomet/57.3.519

G. Mclachlan and T. Krishnan, The EM Algorithm and Extensions, 1997.

G. Mclachlan and D. Peel, Finite Mixture Models, 2000.
DOI : 10.1002/0471721182

N. Nguyen and R. Caruana, Classification with partial labels, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.8-551, 2008.
DOI : 10.1145/1401890.1401958

G. Peters, F. Crespo, P. Lingras, and R. Weber, Soft clustering ??? Fuzzy and rough approaches and their extensions and derivatives, International Journal of Approximate Reasoning, vol.54, issue.2, pp.307-322, 2013.
DOI : 10.1016/j.ijar.2012.10.003

S. Press and S. Wilson, Choosing between Logistic Regression and Discriminant Analysis, Journal of the American Statistical Association, vol.20, issue.364, pp.699-705, 1978.
DOI : 10.1016/0021-9681(67)90082-3

B. Quost, Logistic Regression of Soft Labeled Instances via the Evidential EM Algorithm, Theory and Applications: Third International Conference Proceedings, pp.77-86, 2014.
DOI : 10.1016/0004-3702(94)90026-4

URL : https://hal.archives-ouvertes.fr/hal-01139177

B. Quost and T. Denoeux, Clustering and classification of fuzzy data using the fuzzy EM algorithm, Fuzzy Sets and Systems, vol.286, pp.134-156, 2016.
DOI : 10.1016/j.fss.2015.04.012

URL : https://hal.archives-ouvertes.fr/hal-01294270

E. Ramasso and T. Denoeux, Making Use of Partial Knowledge About Hidden States in HMMs: An Approach Based on Belief Functions, IEEE Transactions on Fuzzy Systems, vol.22, issue.2, pp.1-11, 2013.
DOI : 10.1109/TFUZZ.2013.2259496

URL : https://hal.archives-ouvertes.fr/hal-00834177

C. Richard, Une méthodologie pour la détectiondétectionà structure imposée. applications au plan temps-fréquence, 1998.

C. Richard and R. Lengellé, Data-driven design and complexity control of time???frequency detectors, Signal Processing, vol.77, issue.1, pp.37-48, 1999.
DOI : 10.1016/S0165-1684(99)00021-3

G. Shafer, A mathematical theory of evidence, 1976.

T. Strat, Decision analysis using belief functions, International Journal of Approximate Reasoning, vol.4, issue.5-6, pp.5-6391, 1990.
DOI : 10.1016/0888-613X(90)90014-S

N. Sutton-charani, S. Destercke, and T. Denoeux, Learning Decision Trees from Uncertain Data with an Evidential EM Approach, 2013 12th International Conference on Machine Learning and Applications, pp.111-116, 2013.
DOI : 10.1109/ICMLA.2013.26

URL : https://hal.archives-ouvertes.fr/hal-00933177

N. Sutton-charani, S. Destercke, and T. Denoeux, Training and Evaluating Classifiers from Evidential Data: Application to E2M Decision Tree Pruning, Belief Functions: Theory and Applications: Third International Conference Proceedings, pp.87-94, 2014.
DOI : 10.1109/ICMLA.2013.26

S. Trabelsi, Z. Elouedi, and K. Mellouli, Pruning belief decision tree methods in averaging and conjunctive approaches, International Journal of Approximate Reasoning, vol.46, issue.3, pp.568-595, 2007.
DOI : 10.1016/j.ijar.2007.02.004

L. Zadeh, Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, vol.1, issue.1, pp.3-28, 1978.
DOI : 10.1016/0165-0114(78)90029-5

K. Zhou, A. Martin, Q. Pan, A. Laurent, O. Strauss et al., Evidential-EM Algorithm Applied to Progressively Censored Observations, Processing and Management of Uncertainty in Knowledge-Based Systems: 15th International Conference Proceedings, Part III, pp.180-189, 2014.
DOI : 10.1007/978-3-319-08852-5_19

URL : https://hal.archives-ouvertes.fr/hal-01100840