L. Breiman, Bagging predictors, Machine Learning, vol.10, issue.2, pp.123-140, 1996.
DOI : 10.1007/BF00058655

L. Breiman, Combining predictors, in Combining Artificial Neural Nets, 1999.

B. Efron, Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation, Journal of the American Statistical Association, vol.78, issue.382, pp.316-331, 1983.
DOI : 10.1080/01621459.1983.10477973

B. Efron and R. J. Tibshirani, Improvements on cross-validation: The .632+ bootstrap method, Journal of the American Statistical Association, issue.43, pp.92-548, 1995.

M. J. Embrechts and S. Benedek, Hybrid Identification of Nuclear Power Plant Transients With Artificial Neural Networks, IEEE Transactions on Industrial Electronics, vol.51, issue.3, pp.686-693, 2004.
DOI : 10.1109/TIE.2004.824874

A. Evsukoff and S. Gentil, Recurrent neuro-fuzzy system for fault detection and isolation in nuclear reactors, Advanced Engineering Informatics, vol.19, issue.1, pp.55-66, 2005.
DOI : 10.1016/j.aei.2005.01.009

L. K. Hansen and P. Salamon, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.10, pp.993-1001, 1990.
DOI : 10.1109/34.58871
URL : http://orbit.dtu.dk/en/publications/neural-network-ensembles(492f6c68-703a-4b6d-97bb-8509d817d00f).html

H. Jiawei and M. Kamber, Data Mining-Concepts and Techniques, 2001.

J. M. Keller, P. Gader, H. Tahani, J. H. Chiang, and M. Mohamed, Advances in fuzzy integration for pattern recognition. Fuzzy Sets and Systems, pp.273-283, 1994.

K. Kim and E. B. Bartlett, Nuclear power plant fault diagnosis using neural networks with error estimation, IEEE Transactions on Nuclear Science, vol.43, issue.4, pp.2373-2388, 1996.

R. Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, 1995.

L. I. Kuncheva, Real medical datasets, Informatics, 2005.

J. A. Leonard and M. A. Kramer, Radial basis function networks for classifying process faults, IEEE Control Systems, vol.11, issue.3, pp.31-38, 1991.
DOI : 10.1109/37.75576

K. R. Müller and S. Mika, An introduction to kernel-based learning algorithms, IEEE Transactions on Neural Networks, vol.12, issue.2, pp.181-201, 2001.
DOI : 10.1109/72.914517

B. Parhami, Voting algorithms, IEEE Transactions on Reliability, vol.43, issue.4, pp.617-629, 1994.
DOI : 10.1109/24.370218

R. Polikar, Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, vol.6, issue.3, pp.21-45, 2006.
DOI : 10.1109/MCAS.2006.1688199

R. Polikar, Bootstrap-Inspired Techniques in Computational Intelligence, IEEE signal processing magazine, vol.59, pp.59-72, 2007.
DOI : 10.1109/msp.2007.4286565
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.387.4151

E. Puska and S. Normann, 3-d core studies for hambo simulator In proceedings of presentations on man-machine system research, Enlarged Halden programme group meeting, 2002.

R. Razavi-far, H. Davilu, V. Palade, and C. Lucas, Model-based fault detection and isolation of a steam generator using neuro-fuzzy networks, Neurocomputing, vol.72, issue.13-15, pp.72-2939, 2009.
DOI : 10.1016/j.neucom.2009.04.004

D. Roverso, Soft computing tools for transient classification, Information Sciences, vol.127, issue.3-4, pp.137-156, 2000.
DOI : 10.1016/S0020-0255(00)00035-9

D. Roverso, On-line early fault detection and diagnosis with the alladin transient classifier. Proceedings of PNPIC and HMIT-2004, the 4 th American Nuclear Society, International Topical Meeting on Nuclear Plant Instrumentation Control and Human-Machine Interface Technologies, 2004.

J. Reifman, Survey of artificial intelligence methods for detection and identification of component faults in nuclear power plants, Nuclear Technology, vol.119, pp.76-97, 1997.

H. Schwenk and Y. Bengio, Boosting Neural Networks, Neural Computation, vol.5, issue.2, pp.1869-1887, 2000.
DOI : 10.1162/neco.1992.4.1.1
URL : https://hal.archives-ouvertes.fr/hal-01434666

L. X. Wang and M. Mendel, Generating fuzzy rules by learning from examples, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.6, pp.1414-1427, 1992.
DOI : 10.1109/21.199466

K. Woods, W. P. Kegelmeyer, and K. Bowyer, Combination of multiple classifiers using local accuracy estimates, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.19, issue.4, pp.405-410, 1997.
DOI : 10.1109/34.588027

L. Xu, A. Krzyzak, and C. Y. Suen, 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

B. Yuan and G. Klir, Intelligent Hybrid Systems Fuzzy Logic, Neural Network, and Genetic Algorithms, 1997.

K. Zhao and B. R. Upadhyaya, Adaptive fuzzy inference causal graph approach to fault detection and isolation of field devices in nuclear power plants, Progress in Nuclear Energy, pp.3-4, 2005.
DOI : 10.1016/j.pnucene.2005.03.006

E. Zio, P. Baraldi, and N. Pedroni, Selecting features for nuclear transients classification by means of genetic algorithms, IEEE Transactions on Nuclear Science, vol.53, issue.3, pp.1479-1493, 2006.
DOI : 10.1109/TNS.2006.873868

E. Zio, P. Baraldi, and G. Gola, Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery, Applied Soft Computing, vol.8, issue.4, 2008.
DOI : 10.1016/j.asoc.2007.10.005

E. Zio and P. Baraldi, Identification of nuclear transients via optimized fuzzy clustering, Annals of Nuclear Energy, vol.32, issue.10, pp.1068-1080, 2005.
DOI : 10.1016/j.anucene.2005.02.012

E. Zio and G. Gola, Neuro-fuzzy pattern classification for fault diagnosis in nuclear components, Annals of Nuclear Energy, vol.33, issue.5, pp.415-426
DOI : 10.1016/j.anucene.2005.12.008