P. Garcia-laencina, J. Sancho-gomez, and A. , Pattern classification with missing data: a review, Neural Computing and Applications, vol.24, issue.12, pp.263-282, 2010.
DOI : 10.1007/s00521-009-0295-6

R. J. Little and D. B. Rubin, Statistical Analysis with Missing Data, 1987.
DOI : 10.1002/9781119013563

C. M. Bishop, Pattern recognition and machine learning, 2007.

Z. Ghahramani and M. I. Jordan, Supervised learning from incomplete data via an EM approach, Eds) Adv. Neural Inf. Process, pp.120-127, 1994.

P. K. Sharpe and R. J. Solly, Dealing with missing values in neural network-based diagnostic systems, Neural Computing & Applications, vol.30, issue.11, pp.73-77, 1995.
DOI : 10.1007/BF01421959

J. R. Quinlan, Induction of decision trees, Machine Learning, pp.81-106, 1986.
DOI : 10.1007/BF00116251

R. J. Hathaway and J. C. Bezdek, Fuzzy c-means clustering of incomplete data, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.31, issue.5, pp.735-744, 2001.
DOI : 10.1109/3477.956035

K. Pelckmans, J. D. Brabanter, J. A. Suykens, and B. D. Moor, Handling missing values in support vector machine classifiers, Neural Networks, vol.18, issue.5-6, pp.5-6, 2005.
DOI : 10.1016/j.neunet.2005.06.025

A. Farhangfar, L. Kurgan, and J. Dy, Impact of imputation of missing values on classification error for discrete data, Pattern Recognition, vol.41, issue.12, pp.3692-3705, 2008.
DOI : 10.1016/j.patcog.2008.05.019

D. J. Mundfrom and A. Whitcomb, Imputing missing values: The effect on the accuracy of classification, Multiple Linear Regression Viewpoints, pp.13-19, 1998.

G. Batista and M. C. Monard, A Study of K-Nearest Neighbour as an Imputation Method, Proc. of Second International Conference on Hybrid Intelligent Systems, pp.251-260, 2002.

J. Luengo, J. A. Saez, and F. Herrera, Missing data imputation for fuzzy rule-based classification systems, Soft Computing, vol.9, issue.6, pp.863-881, 2012.
DOI : 10.1007/s00500-011-0774-4

D. Li, J. Deogun, W. Spaulding, and B. Shuart, Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method, 4th international conference of rough sets and current trends in computing (RSCTC04), pp.573-579, 2004.
DOI : 10.1007/978-3-540-25929-9_70

F. Fessant and S. Midenet, Self-Organising Map for Data Imputation and Correction in Surveys, Neural Computing & Applications, vol.10, issue.4, pp.300-310, 2002.
DOI : 10.1007/s005210200002

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

F. Smarandache and J. Dezert, Advances and applications of DSmT for information fusion, 2004.
URL : https://hal.archives-ouvertes.fr/hal-01080187

P. Smets, The combination of evidence in the transferable belief model, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.5, pp.447-458, 1990.
DOI : 10.1109/34.55104

A. Jousselme, C. Liu, D. Grenier, and E. Bossé, Measuring ambiguity in the evidence theory, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.36, issue.5, pp.890-903, 2006.
DOI : 10.1109/TSMCA.2005.853483

H. Laanaya, A. Martin, D. Aboutajdine, and A. Khenchaf, Support vector regression of membership functions and belief functions ??? Application for pattern recognition, Information Fusion, vol.11, issue.4, pp.338-350, 2010.
DOI : 10.1016/j.inffus.2009.12.007

URL : https://hal.archives-ouvertes.fr/hal-00518508

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

L. M. Zouhal and T. Denoeux, An evidence-theoretic k-NN rule with parameter optimization, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.28, issue.2, pp.263-271, 1998.
DOI : 10.1109/5326.669565

Z. Liu, Q. Pan, and J. Dezert, A new belief-based K-nearest neighbor classification method, Pattern Recognition, vol.46, issue.3, pp.834-844, 2013.
DOI : 10.1016/j.patcog.2012.10.001

Z. Liu, Q. Pan, J. Dezert, and G. Mercier, Credal classification rule for uncertain data based on belief functions, Pattern Recognition, vol.47, issue.7, pp.2532-2541, 2014.
DOI : 10.1016/j.patcog.2014.01.011

URL : https://hal.archives-ouvertes.fr/hal-01060091

Z. Liu, Q. Pan, G. Mercier, and J. Dezert, A New Incomplete Pattern Classification Method Based on Evidential Reasoning, IEEE Transactions on Cybernetics, vol.45, issue.4, pp.635-646, 2015.
DOI : 10.1109/TCYB.2014.2332037

URL : https://hal.archives-ouvertes.fr/hal-01162207

T. Denoeux and P. Smets, Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.36, issue.6, pp.1395-1406, 2006.
DOI : 10.1109/TSMCB.2006.877795

T. Denoeux, A neural network classifier based on Dempster-Shafer theory, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.30, issue.2, pp.131-150, 2000.
DOI : 10.1109/3468.833094

X. Deng, Y. Hu, F. T. Chan, S. Mahadevan, and Y. Deng, Parameter estimation based on interval-valued belief structures, European Journal of Operational Research, vol.241, issue.2, pp.579-582, 2015.
DOI : 10.1016/j.ejor.2014.10.002

M. Masson and T. Denoeux, ECM: An evidential version of the fuzzy c-means algorithm, Pattern Recognition, vol.41, issue.4, pp.1384-1397, 2008.
DOI : 10.1016/j.patcog.2007.08.014

Z. Liu, J. Dezert, G. Mercier, Q. Pan, and C. Belief, Belief C-Means: An extension of Fuzzy C-Means algorithm in belief functions framework, Pattern Recognition Letters, vol.33, issue.3, pp.291-300, 2012.
DOI : 10.1016/j.patrec.2011.10.011

URL : https://hal.archives-ouvertes.fr/hal-01058024

Z. Liu, Q. Pan, J. Dezert, and G. Mercier, Credal c-means clustering method based on belief functions, Knowledge-based systems, pp.119-132, 2015.

K. Zhou, A. Martin, Q. Pan, and Z. Liu, Median evidential c-means algorithm and its application to community detection, Knowledgebased systems, pp.69-88, 2015.

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

Z. Liu, J. Dezert, Q. Pan, and G. Mercier, Combination of sources of evidence with different discounting factors based on a new dissimilarity measure, Decision Support Systems, vol.52, issue.1, pp.133-141, 2011.
DOI : 10.1016/j.dss.2011.06.002

S. Huang, X. Su, Y. Hu, S. Mahadevan, and Y. Deng, A new decision-making method by incomplete preferences based on evidence distance, Knowledge-Based Systems, pp.264-272, 2014.

X. Li, J. Dezert, F. Smarandache, and X. Huang, Evidence supporting measure of similarity for reducing the complexity in information fusion???, Information Sciences, vol.181, issue.10, pp.1818-1835, 2011.
DOI : 10.1016/j.ins.2010.10.025

D. Q. Han, Y. Deng, and C. Han, Sequential weighted combination for unreliable evidence based on evidence variance, Decision Support Systems, vol.56, pp.387-393, 2013.
DOI : 10.1016/j.dss.2013.05.004

T. Kohonen, The self-organizing map, Proceedings of the IEEE, vol.78, issue.9, pp.1464-1480, 1990.
DOI : 10.1109/5.58325

D. Dubois and H. Prade, Representation and combination of uncertainty with belief functions and possibility measures, Computational Intelligence, vol.5, issue.1, pp.244-264, 1988.
DOI : 10.1016/0165-0114(78)90029-5

L. A. Zadeh, On the validity of Dempster's rule of combination, 1979.

J. Dezert and A. Tchamova, On the Validity of Dempster's Fusion Rule and its Interpretation as a Generalization of Bayesian Fusion Rule, International Journal of Intelligent Systems, vol.1, issue.2, pp.223-252, 2014.
DOI : 10.1002/int.21638

F. Smarandache and J. Dezert, Information fusion based on new proportional conflict redistribution rules, 2005 7th International Conference on Information Fusion, 2005.
DOI : 10.1109/ICIF.2005.1591955

I. Hammami, J. Dezert, G. Mercier, and A. Hamouda, On the Estimation of Mass Functions Using Self Organizing Maps, Proc. of Belief 2014 Conf, 2014.
DOI : 10.1109/TSMCB.2002.806496

S. Geisser, Predictive inference: an introduction, 1993.
DOI : 10.1007/978-1-4899-4467-2