R. R. Yager and F. Petry, An intelligent quality-based approach to fusing multi-source probabilistic information, Information Fusion, vol.31, pp.127-136, 2016.

F. Herrera, S. Alonso, F. Chiclana, and E. Herrera-viedma, Computing with words in decision making: foundations, trends and prospects, Fuzzy Optimization and Decision Making, vol.8, pp.337-364, 2009.

E. P. Blasch, D. A. Lambert, P. Valin, M. M. Kokar, J. Llinas et al., High Level Information Fusion (HLIF): Survey of models, issues, and grand challenges, vol.27, pp.4-20, 2012.

D. L. Hall and J. M. Jordan, Human-Centered Information Fusion: Artech House, Incorporated, 2010.

P. Walley, Statistical Reasoning With Imprecise Probabilities, 1991.

G. Shafer, A Mathematical Theory of Evidence, 1976.

L. Zadeh, Fuzzy Sets as the Basis for a Theory of Possibility, Fuzzy Sets and Systems, vol.1, pp.3-28, 1978.

R. R. Yager, Hard and soft information fusion using measures, 2010 IEEE International Conference on Intelligent Systems and Knowledge Engineering, pp.13-16, 2010.

M. Liggins, D. Hall, and J. Llinas, Handbook of Multisensor Data Fusion: Theory and Practice, 2008.

D. L. Hall and S. A. Mcmullen, Mathematical Techniques in Multisensor Data Fusion: Artech House, 2004.

B. Khaleghi, A. Khamisa, F. O. Karraya, and S. N. Razavi, Multisensor data fusion: A review of the state-of-the-art, Information Fusion, vol.14, pp.28-44, 2013.

S. Das, High-Level Data Fusion: Artech House, 2008.

E. Blasch, É. Bossé, and D. A. Lambert, High-Level Information Fusion Management and Systems Design. Artech House, p.p.^pp, 2012.

D. Dubois, W. Liu, J. Ma, and H. Prade, The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks, Information Fusion, vol.32, pp.12-39, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01484952

L. Snidaro, J. Garcia-herrera, J. Llinas, and E. Blasch, Context-Enhanced Information Fusion, Boosting Real-World Performance with Domain Knowledge, 2016.

J. Tacnet, S. Carladous, J. Dezert, and M. Batton-hubert, New integrated and multiscale decision-aiding framework in a context of imperfect information: application to the assessment of torrent checkdams' effectiveness, EGU General Assembly Conference Abstracts, p.4440, 2017.

J. Tacnet, M. Batton-hubert, and J. Dezert, A two-step fusion process for multi-criteria decision applied to natural hazards in mountains, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00558279

, Information Quality in Information Fusion and Decision Making (Information Fusion and Data Science, p.p.^pp, 2019.

S. Calderwood, K. Mcareavey, W. Liu, and J. Hong, Context-dependent combination of sensor information in Dempster-Shafer theory for BDI, Knowledge and Information Systems, vol.51, pp.259-285, 2017.

G. J. Klir and J. F. Geer, Information-Preserving Probability-Possibility Transformations, Fuzzy logic, pp.417-428, 1993.

D. Dubois and E. Hüllermeier, A notion of comparative probabilistic entropy based on the possibilistic specificity ordering, European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, pp.848-859, 2005.

P. Elmore and F. Petry, Information Theory Applications in Soft Computing, Granular, Soft and Fuzzy Approaches for Intelligent Systems, pp.81-97, 2017.

R. R. Yager, On prioritized multiple-criteria aggregation, IEEE Transactions on Systems, Man, and Cybernetics, vol.42, pp.1297-1305, 2012.

R. A. Fisher, IRIS data set

S. Lekkas and L. Mikhailov, Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases, Artificial Intelligence in Medicine, vol.50, pp.117-126, 2010.

P. I. , , 2019.

K. Bache and M. Lichman, Glass Data Set Available, p.28, 2013.

K. Bache and M. Lichman, Liver-disorders data set, p.28, 2013.

D. Chang, A. H. Desoky, M. Ouyang, and E. C. Rouchka, Compute pairwise manhattan distance and pearson correlation coefficient of data points with gpu, 2009 10th ACIS International Conference on Software Engineering, Artificial Intelligences, Networking and Parallel/Distributed Computing, pp.501-506, 2009.

D. Dubois, H. Prade, and S. Sandri, On possibility/probability transformations, Fuzzy logic, pp.103-112, 1993.

A. J. Pinar, D. T. Anderson, T. C. Havens, A. Zare, and T. Adeyeba, Measures of the Shapley index for learning lower complexity fuzzy integrals, Granular Computing, vol.2, pp.303-319, 2017.

S. A. Bouhamed, I. K. Kallel, D. S. Masmoudi, and B. Solaiman, Feature selection in possibilistic modeling, Pattern Recognition, vol.48, pp.3627-3640, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01474065

P. Agarwal and H. , Possibility theory versus probability theory in fuzzy measure theory, Int. Journal of Engineering Research and Applications, vol.5, pp.37-43, 2015.

I. K. Kallel, S. Almouahed, B. Solaiman, and É. Bossé, An iterative possibilistic knowledge diffusion approach for blind medical image segmentation, Pattern Recognition, vol.78, pp.182-197, 2018.

B. Alsahwa, B. Solaiman, S. Almouahed, E. Bosse, and D. Gueriot, Iterative refinement of possibility distributions by learning for pixel-based classification, IEEE Transactions on Image Processing, vol.25, pp.3533-3545, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01370053

B. Alsahwa, B. Solaiman, É. Bossé, S. Almouahed, and D. Gueriot, A method of spatial unmixing based on possibilistic similarity in soft pattern classification, Fuzzy information and engineering, vol.8, pp.295-314, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01424536

I. K. Kallel, S. Almouahed, B. Alsahwa, and B. Solaiman, The use of contextual spatial knowledge for low-quality image segmentation, Multimedia Tools and Applications, pp.1-21, 2018.

R. B. Bhatt, G. Sharma, A. Dhall, and S. Chaudhury, Efficient skin region segmentation using low complexity fuzzy decision tree model, pp.1-4, 2009.

A. Dhall, G. Sharma, R. Bhatt, and G. M. Khan, Adaptive Digital Makeup, Proc. of International Symposium on Visual Computing (ISVC), vol.5876, pp.728-736, 2009.