Constructing consonant belief functions from sample data using confidence sets of pignistic probabilities, International Journal of Approximate Reasoning, vol.49, issue.3, pp.575-594, 2008. ,
DOI : 10.1016/j.ijar.2008.06.002
The combination of multiple classifiers using an evidential reasoning approach, Artificial Intelligence, vol.172, issue.15, pp.1731-1751, 2008. ,
DOI : 10.1016/j.artint.2008.06.002
A fuzzy random forest, International Journal of Approximate Reasoning, vol.51, issue.7, pp.729-747, 2010. ,
DOI : 10.1016/j.ijar.2010.02.003
Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996. ,
DOI : 10.1007/BF00058655
Random forests, Machine Learning, pp.5-32, 2001. ,
Classification and Regression Trees, 1984. ,
Diversity creation methods: a survey and categorisation, Information Fusion, vol.6, issue.1, pp.5-20, 2005. ,
DOI : 10.1016/j.inffus.2004.04.004
Combining multiple neural networks by fuzzy integral for robust classification, IEEE Transactions on System, Man and Cybernetics, vol.25, issue.2, pp.380-384, 1995. ,
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 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
Analysis of evidence-theoretic decision rules for pattern classification, Pattern Recognition, vol.30, issue.7, pp.1095-1107, 1997. ,
DOI : 10.1016/S0031-3203(96)00137-9
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
The cautious rule of combination for belief functions and some extensions, 2006 9th International Conference on Information Fusion, 2006. ,
DOI : 10.1109/ICIF.2006.301572
Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence, Artificial Intelligence, vol.172, issue.2-3, pp.234-264, 2008. ,
DOI : 10.1016/j.artint.2007.05.008
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
Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms, Neural Computation, vol.6, issue.7, pp.1895-1923, 1998. ,
DOI : 10.1007/BF00058655
Ensemble Methods in Machine Learning, Multiple classifier systems, pp.1-15, 2000. ,
DOI : 10.1007/3-540-45014-9_1
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization, Machine Learning, pp.139-157, 2000. ,
A SET-THEORETIC VIEW OF BELIEF FUNCTIONS Logical operations and approximations by fuzzy sets???, International Journal of General Systems, vol.1, issue.3, pp.193-226, 1986. ,
DOI : 10.1016/0165-0114(78)90029-5
A definition of subjective possibility, International Journal of Approximate Reasoning, vol.48, issue.2, pp.352-364, 2008. ,
DOI : 10.1016/j.ijar.2007.01.005
Assessing Sensor Reliability for Multisensor Data Fusion Within the Transferable Belief Model, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.782-787, 2004. ,
DOI : 10.1109/TSMCB.2003.817056
Cluster and Select Approach to Classifier Fusion, Advances in Data Analysis, pp.59-66, 2007. ,
DOI : 10.1007/978-3-540-70981-7_7
Evaluating Sensor Reliability in Classification Problems Based on Evidence Theory, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.36, issue.5, pp.970-981, 2006. ,
DOI : 10.1109/TSMCB.2006.872269
Hierarchical fusion of expert opinions in the Transferable Belief Model, application to climate sensitivity, International Journal of Approximate Reasoning, vol.49, issue.3, pp.555-574, 2008. ,
DOI : 10.1016/j.ijar.2008.05.003
Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.10, pp.993-1000, 1990. ,
DOI : 10.1109/34.58871
Algorithms for clustering data, 1988. ,
A new distance between two bodies of evidence, Information Fusion, vol.2, issue.2, pp.91-101, 2001. ,
DOI : 10.1016/S1566-2535(01)00026-4
Combination of partially non-distinct beliefs: The cautious-adaptive rule, International Journal of Approximate Reasoning, vol.50, issue.7, pp.1000-1021, 2009. ,
DOI : 10.1016/j.ijar.2009.03.006
On combining classifiers, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.3, pp.226-239, 1998. ,
DOI : 10.1109/34.667881
Hierarchical and conditional combination of belief functions induced by visual tracking, International Journal of Approximate Reasoning, vol.51, issue.4, pp.410-428, 2010. ,
DOI : 10.1016/j.ijar.2009.12.001
URL : https://hal.archives-ouvertes.fr/hal-00595038
Triangular norms, 2000. ,
DOI : 10.1007/978-94-015-9540-7
Decision templates for multiple classifier fusion: an experimental comparison, Pattern Recognition, vol.34, issue.2, pp.299-314, 2001. ,
DOI : 10.1016/S0031-3203(99)00223-X
An experimental study on diversity for bagging and boosting with linear classifiers, Information Fusion, vol.3, issue.4, pp.245-258, 2002. ,
DOI : 10.1016/S1566-2535(02)00093-3
Measures of diversity in classifier ensembles, Machine Learning, pp.181-207, 2003. ,
Combining classifiers for word sense disambiguation based on Dempster???Shafer theory and OWA operators, Data & Knowledge Engineering, vol.63, issue.2, pp.381-396, 2007. ,
DOI : 10.1016/j.datak.2007.03.013
Combining the Classification Results of Independent Classifiers Based on the Dempster/Shafer Theory of Evidence, Pattern Recognition and Artificial Intelligence, vol.10, pp.381-393, 1988. ,
DOI : 10.1016/B978-0-444-87137-4.50032-1
RECM: Relational evidential c-means algorithm, Pattern Recognition Letters, vol.30, issue.11, pp.1015-1026, 2009. ,
DOI : 10.1016/j.patrec.2009.04.008
URL : https://hal.archives-ouvertes.fr/hal-00400273
Discriminant Analysis and Statistical Pattern Recognition, 1992. ,
DOI : 10.1002/0471725293
Fusion of multi-level decision systems using the transferable belief model, 2005 7th International Conference on Information Fusion, 2005. ,
DOI : 10.1109/ICIF.2005.1591952
Belief functions contextual discounting and canonical decompositions, International Journal of Approximate Reasoning, vol.53, issue.2, 2010. ,
DOI : 10.1016/j.ijar.2011.06.005
Refined modeling of sensor reliability in the belief function framework using contextual discounting, Information Fusion, vol.9, issue.2, pp.246-258, 2008. ,
DOI : 10.1016/j.inffus.2006.08.001
T-norm and uninorm-based combination of belief functions, NAFIPS 2008, 2008 Annual Meeting of the North American Fuzzy Information Processing Society, pp.19-22, 2008. ,
DOI : 10.1109/NAFIPS.2008.4531209
The Unnormalized Dempster???s Rule of Combination: A New Justification from the Least Commitment Principle and Some Extensions, Journal of Automated Reasoning, vol.80, issue.4, pp.61-87, 2010. ,
DOI : 10.1007/s10817-009-9152-7
Aggregating multiple classification results using fuzzy integration and stochastic feature selection, International Journal of Approximate Reasoning, vol.51, issue.8 ,
DOI : 10.1016/j.ijar.2010.05.003
URL : http://doi.org/10.1016/j.ijar.2010.05.003
Pairwise classifier combination using belief functions, Pattern Recognition Letters, vol.28, issue.5, pp.644-653, 2007. ,
DOI : 10.1016/j.patrec.2006.11.002
URL : https://hal.archives-ouvertes.fr/hal-00445480
Adapting a combination rule to nonindependent information sources, Proceedings of the 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'08), pp.448-455, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00338906
Refined classifier combination using belief functions, Proceedings of the 10th International Conference on Information Fusion (Fusion'08), pp.776-782, 2008. ,
DOI : 10.1016/j.patrec.2006.11.002
URL : https://hal.archives-ouvertes.fr/hal-00338899
Building ensemble classifiers using belief functions and OWA operators, Soft Computing, vol.28, issue.2, pp.543-558, 2008. ,
DOI : 10.1007/s00500-007-0227-2
Combining the results of several neural network classifiers, Neural Networks, vol.7, issue.5, pp.777-781, 1994. ,
DOI : 10.1016/0893-6080(94)90099-X
New Measure of Classifier Dependency in Multiple Classifier Systems, Multiple classifier systems, pp.127-136, 2002. ,
DOI : 10.1007/3-540-45428-4_13
Classifier selection for majority voting, Information Fusion, vol.6, issue.1, pp.63-81, 2005. ,
DOI : 10.1016/j.inffus.2004.04.008
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.6008
Boosting the margin: a new explanation for the effectiveness of voting methods, The Annals of Statistics, vol.26, issue.5, pp.1651-1686, 1998. ,
DOI : 10.1214/aos/1024691352
A mathematical theory of evidence, 1976. ,
Combining diverse neural nets, The Knowledge Engineering Review, vol.12, issue.3, pp.231-247, 1997. ,
DOI : 10.1017/S0269888997003123
The sources of increased accuracy for two proposed boosting algorithms, Proceedings of the American Association for Artificial Intelligence (AAAI'96), Integrating Multiple Learned Models Workshop, pp.120-125, 1996. ,
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
Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem, International Journal of Approximate Reasoning, vol.9, issue.1, pp.1-35, 1993. ,
DOI : 10.1016/0888-613X(93)90005-X
The canonical decomposition of a weighted belief, Proceedings of the International Joint Conferences in Artificial Intelligence, pp.1896-1901, 1995. ,
The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-234, 1994. ,
DOI : 10.1016/0004-3702(94)90026-4
URL : https://hal.archives-ouvertes.fr/hal-01185821
Error Correlation and Error Reduction in Ensemble Classifiers, Connection Science, vol.8, issue.3-4, pp.3-4385, 1996. ,
DOI : 10.1080/095400996116839
Incremental construction of classifier and discriminant ensembles, Information Sciences, vol.179, issue.9, pp.1298-1318, 2009. ,
DOI : 10.1016/j.ins.2008.12.024
Stacked generalization, Neural Networks, vol.5, issue.2, pp.241-259, 1992. ,
DOI : 10.1016/S0893-6080(05)80023-1
Methods of combining multiple classifiers and their applications to handwriting recognition, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.3, pp.418-435, 1992. ,
DOI : 10.1109/21.155943
The combination of multiple classifiers by a neural network approach, Int. J. Pattern Recognition and Artificial Intelligence, vol.9, issue.3, pp.579-597, 1995. ,