Y. Benjamini and W. Liu, A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence, Journal of Statistical Planning and Inference, vol.82, issue.1-2, pp.163-170, 1999.
DOI : 10.1016/S0378-3758(99)00040-3

H. Blockeel, D. Raedt, L. , and R. J. , Top-down induction of clustering trees, Proceedings of the 15th International Conference on Machine Learning, pp.55-63, 1998.

E. Bloedorn and R. Michalski, Data-driven constructive induction, IEEE Intelligent Systems, vol.13, issue.2, pp.30-37, 1998.
DOI : 10.1109/5254.671089

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.638

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

G. Dunteman, Principal components analysis An introduction to probability theory and its applications Resampling-based multiple testing for microarray data analysis, SAGE publications, Inc Feller W Test, vol.69, issue.121, pp.1-77, 1950.

G. Gomez and E. Morales, Automatic feature construction and a simple rule induction algorithm for skin detection, Proc. of the ICML workshop on Machine Learning in Computer Vision, pp.31-38, 2002.

S. Holm, A simple sequentially rejective multiple test procedure, Scandinavian journal of statistics pp, pp.65-70, 1979.

X. Huo, X. Ni, and A. Smith, A survey of manifold-based learning methods. Mining of Enterprise Data pp, pp.6-10, 2005.

S. Lallich and R. Rakotomalala, Fast feature selection using partial correlation for multivalued attributes, Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp.221-231, 2000.

S. Lallich, O. Teytaud, and E. Prudhomme, Statistical inference and data mining: false discoveries control, COMPSTAT: proceedings in computational statistics: 17th symposium, p.325, 2006.
DOI : 10.1007/978-3-7908-1709-6_25

URL : https://hal.archives-ouvertes.fr/hal-00113593

H. Liu and H. Motoda, Feature extraction, construction and selection: A data mining perspective, 1998.
DOI : 10.1007/978-1-4615-5725-8

C. Matheus, Adding domain knowledge to sbl through feature construction, Proceedings of the Eighth National Conference on Artificial Intelligence, pp.803-808, 1990.

R. Michalski, A theory and methodology of inductive learning, Artificial Intelligence, vol.20, issue.2, pp.111-161, 1983.
DOI : 10.1016/0004-3702(83)90016-4

D. Mo and S. Huang, Feature selection based on inference correlation, Intelligent Data Analysis, vol.15, issue.3, pp.375-398, 2011.

H. Motoda and H. Liu, Feature selection, extraction and construction. Communication of IICM (Institute of Information and Computing Machinery, pp.67-72, 2002.

P. Murphy and M. Pazzani, ID2-of-3: Constructive Induction of M-of-N Concepts for Discriminators in Decision Trees, Proceedings of the Eighth International Workshop on Machine Learning, pp.183-187, 1991.
DOI : 10.1016/B978-1-55860-200-7.50040-4

G. Pagallo and D. Haussler, Boolean feature discovery in empirical learning, Machine Learning, vol.5, issue.1, pp.71-99, 1990.
DOI : 10.1023/A:1022611825350

G. Piatetsky-shapiro, Discovery, analysis, and presentation of strong rules, Knowledge discovery in databases, vol.229, pp.229-248, 1991.

J. Quinlan, Induction of decision trees, Machine Learning, vol.1, issue.1, pp.81-106, 1986.
DOI : 10.1007/BF00116251

J. Quinlan, B. Morgan-kaufmann-russell, A. Torralba, K. Murphy, and W. Freeman, C4.5: programs for machine learning Labelme: a database and webbased tool for image annotation, International Journal of Computer Vision, vol.77, issue.1, pp.157-173, 1993.

Y. Sawaragi, H. Nakayama, and T. Tanino, Theory of multiobjective optimization A direct approach to false discovery rates, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.176, issue.643, pp.479-498, 1985.

D. Yang, L. Rendell, and G. Blix, A scheme for feature construction and a comparison of empirical methods, Proceedings of the Twelfth International Joint Conference on Artificial Intelligence, pp.699-704, 1991.

Z. Zheng, Constructing nominal x-of-n attributes, Proceedings of International Joint Conference On Artificial Intelligence, pp.1064-1070, 1995.

Z. Zheng, A comparison of constructive induction with different types of new attribute Constructing conjunctions using systematic search on decision trees, Tech. rep., School of Computing and Mathematics Geelong Zheng Z Knowledge-Based Systems, vol.10, issue.7, pp.421-430, 1996.