R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2012.

D. Dubois, P. Hájek, and H. Prade, Knowledge-driven versus data-driven logics, Journal of Logic, Language and Information, vol.9, issue.1, pp.65-89, 2000.
DOI : 10.1023/A:1008370109997

D. Codetta-raiteri and L. Portinale, Dynamic Bayesian Networks for Fault Detection, Identification, and Recovery in Autonomous Spacecraft, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.45, issue.1, pp.13-24, 2015.
DOI : 10.1109/TSMC.2014.2323212

E. Alpaydin, Introduction to Machine Learning, 2014.

J. Xu, Rule-based automatic software performance diagnosis and improvement, Performance Evaluation, vol.69, issue.11, pp.525-550, 2012.
DOI : 10.1016/j.peva.2009.11.003

C. S. Lee and M. H. Wang, A fuzzy expert system for diabetes decision support application, IEEE Trans. Syst., Man, Cybern. B, Cybern, vol.41, issue.1, pp.139-153, 2011.

D. D. Wu, D. L. Olson, and C. Luo, A Decision Support Approach for Accounts Receivable Risk Management, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.44, issue.12, pp.1624-1632, 2014.
DOI : 10.1109/TSMC.2014.2318020

P. Dasgupta, A Multiagent Swarming System for Distributed Automatic Target Recognition Using Unmanned Aerial Vehicles, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.38, issue.3, pp.549-563, 2008.
DOI : 10.1109/TSMCA.2008.918619

L. Jiao, Q. Pan, X. Feng, and F. Yang, A Belief-Rule-Based Inference Method for Carrier Battle Group Recognition, Lecture Notes in Electrical Engineering, pp.261-271, 2013.
DOI : 10.1007/978-3-642-38524-7_28

URL : https://hal.archives-ouvertes.fr/hal-01081012

P. H. Tsai, Y. J. Lin, Y. Z. Ou, E. T. Chu, and J. W. Liu, A Framework for Fusion of Human Sensor and Physical Sensor Data, IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol.44, issue.9, pp.1248-1261, 2014.
DOI : 10.1109/TSMC.2014.2309090

Z. J. Zhou, C. H. Hu, J. B. Yang, D. L. Xu, M. Y. Chen et al., A sequential learning algorithm for online constructing belief-rule-based systems, Expert Systems with Applications, vol.37, issue.2, pp.1790-1799, 2010.
DOI : 10.1016/j.eswa.2009.07.067

W. Tang, K. Z. Mao, L. O. Mak, G. W. Ng, Z. Sun et al., Target classification using knowledge-based probabilistic model, Proc. 2011 FUSION, pp.1-8, 2011.

W. Tang, K. Z. Mao, L. O. Mak, and G. W. Ng, Adaptive fuzzy rule-based classification system integrating both expert knowledge and data, Proc. 2012 ICTAI, pp.2012-814

C. Tsang, S. Kwong, and H. Wang, Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection, Pattern Recognition, vol.40, issue.9, pp.2373-2391, 2007.
DOI : 10.1016/j.patcog.2006.12.009

A. Quteishat, C. P. Lim, and S. T. Kay, A Modified Fuzzy Min–Max Neural Network With a Genetic-Algorithm-Based Rule Extractor for Pattern Classification, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.40, issue.3, pp.641-650, 2010.
DOI : 10.1109/TSMCA.2010.2043948

J. Sanz, A. Fernández, H. Bustince, and F. Herrera, A genetic tuning to improve the performance of Fuzzy Rule-Based Classification Systems with Interval-Valued Fuzzy Sets: Degree of ignorance and lateral position, International Journal of Approximate Reasoning, vol.52, issue.6, pp.751-766, 2011.
DOI : 10.1016/j.ijar.2011.01.011

J. A. Sanz, M. Galar, A. Jurio, A. Brugos, M. Pagola et al., Medical diagnosis of cardiovascular diseases using an interval-valued fuzzy rule-based classification system, Applied Soft Computing, vol.20, issue.1, pp.103-111, 2014.
DOI : 10.1016/j.asoc.2013.11.009

R. Lowen, Fuzzy Set Theory: Basic Concepts, Techniques and Bibliography, 2012.
DOI : 10.1007/978-94-015-8741-9

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

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

J. B. Yang, J. Liu, J. Wang, H. S. Sii, and H. W. Wang, Belief rule-based inference methodology using the evidential reasoning approach? RIMER, IEEE Trans. Syst, vol.36, issue.2, pp.266-285, 2006.

G. L. Kong, D. L. Xu, and B. Richard, A belief rule-based decision support system for clinical risk assessment of cardiac chest pain, European Journal of Operational Research, vol.219, issue.3, pp.564-573, 2012.
DOI : 10.1016/j.ejor.2011.10.044

B. Li, H. W. Wang, J. B. Yang, M. Guo, and C. Qi, A belief-rule-based inventory control method under nonstationary and uncertain demand, Expert Systems with Applications, vol.38, issue.12, pp.14-997, 2011.
DOI : 10.1016/j.eswa.2011.05.047

Z. J. Zhou, C. H. Hu, J. B. Yang, D. L. Xu, and D. H. Zhou, Online updating belief rule based system for pipeline leak detection under expert intervention, Expert Systems with Applications, vol.36, issue.4, pp.7700-7709, 2009.
DOI : 10.1016/j.eswa.2008.09.032

L. Jiao, Q. Pan, T. Denoeux, Y. Liang, and X. Feng, Belief rule-based classification system: Extension of FRBCS in belief functions framework, Information Sciences, vol.309, issue.1, pp.26-49, 2015.
DOI : 10.1016/j.ins.2015.03.005

URL : https://hal.archives-ouvertes.fr/hal-01141281

P. Smets, Decision making in the TBM: the necessity of the pignistic transformation, International Journal of Approximate Reasoning, vol.38, issue.2, pp.133-147, 2005.
DOI : 10.1016/j.ijar.2004.05.003

H. Helbig, Knowledge Representation and the Semantics of Natural Language, 2006.

O. Dieste and N. Juristo, Systematic review and aggregation of empirical studies on elicitation techniques, IEEE Transactions on Software Engineering, vol.37, issue.2, pp.283-304, 2011.
DOI : 10.1109/TSE.2010.33

O. Cordón, M. José-del-jesus, and F. Herrera, A proposal on reasoning methods in fuzzy rule-based classification systems, International Journal of Approximate Reasoning, vol.20, issue.1, pp.21-45, 1999.
DOI : 10.1016/S0888-613X(00)88942-2

B. Ristic and P. Smets, Target classification approach based on the belief function theory, IEEE Transactions on Aerospace and Electronic Systems, vol.41, issue.2, pp.574-583, 2005.
DOI : 10.1109/TAES.2005.1468749

H. Ishibuchi, K. Nozaki, and H. Tanaka, Distributed representation of fuzzy rules and its application to pattern classification, Fuzzy Sets and Systems, vol.52, issue.1, pp.21-32, 1992.
DOI : 10.1016/0165-0114(92)90032-Y

L. Jiao, T. Denoeux, and Q. Pan, Evidential Editing K-Nearest Neighbor Classifier, Lecture Notes in Computer Science, vol.8, pp.461-471, 2015.
DOI : 10.1007/s10044-015-0452-8

J. A. Sáez, M. Galar, J. Luengo, and F. Herrera, Tackling the problem of classification with noisy data using Multiple Classifier Systems: Analysis of the performance and robustness, Information Sciences, vol.247, issue.1, pp.1-20, 2013.
DOI : 10.1016/j.ins.2013.06.002

C. Liang, Y. Zhang, and Q. Song, Decision tree for dynamic and uncertain data streams, Proc. 2010 ACML, pp.209-224, 2010.

S. Chen and H. He, Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach, Evolving Systems, vol.8, issue.3???4, pp.35-50, 2011.
DOI : 10.1007/s12530-010-9021-y

T. R. Hoens, R. Polikar, and N. V. Chawla, Learning from streaming data with concept drift and imbalance: an overview, Progress in Artificial Intelligence, vol.23, issue.1, pp.89-101, 2012.
DOI : 10.1007/s13748-011-0008-0