M. Basseville and I. V. Nikiforov, Detection of abrupt changes: theory and applications, Journal of the Royal Statistical Society-Series A Statistics in Society, vol.158, p.185, 1995.

L. Breiman, Random forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees. wadsworth & brooks, 1984.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3, pp.41-56, 2009.
DOI : 10.1145/1541880.1541882

E. Côme, M. Cottrell, M. Verleysen, and J. Lacaille, Aircraft engine health monitoring using selforganizing maps, Advances in data mining. applications and theoretical aspects, pp.405-417, 2010.

X. Flandrois, J. Lacaille, J. Masse, and A. Ausloos, Expertise Transfer and Automatic Failure Classification for the Engine Start Capability System, AIAA Infotech@Aerospace Conference, 2009.
DOI : 10.2514/6.2009-1859

F. Fleuret, Fast binary feature selection with conditional mutual information, Journal of Machine Learning Research, vol.5, pp.1531-1555, 2004.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, The Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003.

J. Hegedus, Y. Miche, A. Ilin, and A. Lendasse, Methodology for behavioral-based malware analysis and detection using random projections and knearest neighbors classifiers, Seventh International Conference on, pp.1016-1023, 2011.

N. Japkowicz and S. Stephen, The class imbalance problem: A systematic study, Intelligent data analysis, vol.6, pp.429-449, 2002.

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, 2009.

S. B. Kotsiantis, I. Zaharakis, and P. Pintelas, Supervised machine learning: A review of classification techniques, 2007.

J. Lacaille, A maturation environment to develop and manage health monitoring algorithms, 2009.

H. Peng, F. Long, and C. Ding, Feature selection based on mutual information criteria of maxdependency , max-relevance, and min-redundancy. Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.27, pp.1226-1238, 2005.