M. Abreu and M. Fairhurst, An Empirical Comparison of Individual Machine Learning Techniques in Signature and Fingerprint Classification, 2008.
DOI : 10.1007/978-3-540-89991-4_14

E. A. Akadi, E. A. Ouardighi, and D. Aboutajdine, A Powerful Feature Selection approach based on Mutual Information, International Journal of Computer Science and Network Security, vol.8, issue.4, pp.116-121, 2008.

H. Almuallim and T. Dieggerich, Learning Boolean concepts in the presence of many irrelevant features, Artificial Intelligence, vol.69, issue.1-2, pp.279-306, 1994.
DOI : 10.1016/0004-3702(94)90084-1

H. Almuallim and G. T. Dietterich, Efficient Algorithms for Identifying Relevant Features, Ninth Canadian Conference on Artificial Intelligence, 1992.

M. Ankerst, G. Kastenmoller, H. Kriegel, and T. Seidl, Nearest Neighbor Classification in 3D Protein Databases, Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, 1999.

I. Atmosukarto, 3D Shape Analysis for Quantification, Classification, and Retrieval, 2010.

K. H. Ault, 3D Gemoetric Modeiling for the 21st Centry, Engineering Design Graphics Journal, vol.63, issue.2, pp.33-42, 1999.

L. C. Bajaj, J. Chen, and G. Xu, Free Form Surface Design with A-Patches, Graphics Interface, pp.174-181, 1994.

A. Barbier, E. Galin, and S. Akkouche, Complex Skeletal Implicit Surfaces with Levels of Detail, Journal of WSCG, vol.12, pp.1-3, 2004.

G. Bardis, D. Makris, V. Golfinopoulos, G. Miaoulis, and D. Plemenos, A Formal Framework for Declarative Scene Description Transformation into Geometric Contraints, pp.347-356, 2011.

Z. Barutcuoglu, E. R. Schapire, and T. G. Olga, Hierarchical multi-label prediction of gene function, Bioinformatics, vol.22, issue.7, pp.830-836, 2006.
DOI : 10.1093/bioinformatics/btk048

C. Bielza, G. Li, and P. Larranaga, Multi-dimensional classification with Bayesian networks, International Journal of Approximate Reasoning, vol.52, issue.6, pp.705-727, 2011.
DOI : 10.1016/j.ijar.2011.01.007

H. Blockeel, D. L. Raedt, and J. Ramon, Top-down induction of clustering trees, Proccedings of the 15th International Conference on Machine Learning, 1998.

J. Bloomenthal, Implicit Surfaces, 1999.

R. M. Boutell, J. Luo, X. Shen, and M. C. Brown, Learning multi-label scene classification, Pattern Recognition, vol.37, issue.9, pp.1757-1771, 2007.
DOI : 10.1016/j.patcog.2004.03.009

M. Bramer, Knowledge Discovery and Data Mining, 1999.
DOI : 10.1049/PBPC001E

R. Caruana and D. Freitag, Greedy Attribute Selection, Machine Learning: Proceedings of the Eleventh International Conference, 1994.
DOI : 10.1016/B978-1-55860-335-6.50012-X

R. Caruana and A. Niculescu-mizil, An empirical comparison of supervised learning algorithms, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143865

E. C. Catalano, I. Ivrissimthis, and A. Nasri, Subdivision Surfaces and Applications, Shape Analysis and Structuring, pp.115-143, 2007.
DOI : 10.1007/978-3-540-33265-7_4

S. Chasen, A little history of C4 -the CAD/CAM handbook, 1996.

D. Chauvat, Le projet VoluFormes : un exemple de modélisation déclarative avec controle spatial, 1994.

A. Clare and K. D. Ross, Knowledge Discovery in Multi-label Phenotype Data, Proceedings of European Conference on PKDD, 2001.
DOI : 10.1007/3-540-44794-6_4

W. W. Cohen, Efficient pruning methods for separate-and-conquer rule learning systems, Proceedings of the 13th International Joint Conference of Artificial Intelligence, 1993.

W. W. Cohen, Fast Effective Rule Induction, Machine Learning: Proceeding of the Twelfth International Conference, 1995.
DOI : 10.1016/B978-1-55860-377-6.50023-2

W. W. Cohen and Y. Singer, A simple, fast, and effective rule learner, Proceedings of Annual Conference of American Association for Artificial Intelligence, 1999.

D. F. Comité, R. Gilleron, and M. Tommasi, Learning multi-label alternating decision trees from texts and data, The 3rd International Conference on Machine Learning and Data Mining Pattern Recognition, 2003.

C. P. Conilione and D. Wang, Automatic localization and annotation of facial features using machine learning techniques, Soft Computing, vol.23, issue.3, pp.1231-1245, 2011.
DOI : 10.1007/s00500-010-0586-y

K. Crammer and Y. Singer, A family of additive online algorithms for category ranking, Journal of Machine Learning Research, vol.3, pp.1025-1058, 2003.

F. P. Danglade and P. Veron, On the use of Machine Learning to Defeature CAD Models for Simulation, Computer-Aided Design and Applications, vol.34, issue.2, pp.358-368, 2014.
DOI : 10.1080/16864360.2004.10738315

URL : https://hal.archives-ouvertes.fr/hal-01015339

M. Daniel and M. Lucas, Towards Declarative Geometric Modelling in Mechanics, Integrated Design and Manufacturing in Mechanical Engeneering, pp.427-436, 1997.
DOI : 10.1007/978-94-011-5588-5_43

W. Dankwort and G. Podehl, A New Aesthetic Design Workflow???Results from the European Project FIORES, CAD Tools and Algorithms for Product Design, pp.16-30, 2000.
DOI : 10.1007/978-3-662-04123-9_2

M. Dash and H. Liu, Feature selection for classification, Intelligent Data Analysis, vol.1, issue.1-4, pp.131-156, 1997.
DOI : 10.1016/S1088-467X(97)00008-5

K. C. Dembczynski and E. Hullermeier, Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains, 27th International Conference on Machine Learning

T. Derose, M. Kass, and T. Truong, Subdivision surfaces in character animation, Proceedings of the 25th annual conference on Computer graphics and interactive techniques , SIGGRAPH '98, 1998.
DOI : 10.1145/280814.280826

M. Desjardins, E. Eaton, and L. K. Wagstaff, Learning user preferences for sets of objects, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143879

E. Desmontils, Les modeleurs déclaratifs, Nantes: RR, 1995.

H. Dunham and M. , Data Mining: Introductory and Advanced Topic, 2003.

S. D. Ebert, Advanced Geometric Modelling, 1997.

S. D. Ebert, F. K. Musgrave, P. Darwyn, K. Perlin, and S. Worley, Texturing and Modeling: A Procedural Approach, 2003.

S. D. Ebert, R. Rohrer, D. C. Shaw, P. Panda, M. J. Kukla et al., Procedural shape generation for multi-dimensional data visualization, Computers & Graphics, vol.24, issue.3, pp.375-384, 2000.
DOI : 10.1016/S0097-8493(00)00033-9

G. Eckel and K. Jones, 12 07). techpubs library. (Silicon Graphics) Consulté le 04 18, 2015, sur http://techpubs.sgi.com/library/tpl/cgibin/getdoc .cgi?coll=0650&db=bks&srch=&fname=/SGI_Developer/Perf_PG/sgi_html /ch09 Introduction to Data Mining and Knowledge Discovery, A. H, 1999.

G. Farin, Curves and Surfaces for Computer Aided Geomtric Design: A Practical Guid, 1996.

U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, From Data Mining to Knowledge Discovery: An Overview, American Association for Arificial Intellegence, pp.1-34, 1996.

J. A. Ferreira and A. T. Figueiredo, Efficient feature selection filters for high-dimensional data, Pattern Recognition Letters, vol.33, issue.13, pp.1794-1804, 2012.
DOI : 10.1016/j.patrec.2012.05.019

J. Flores, J. Gamez, and A. Martinez, Domains of competence of the semi-naive Bayesian network classifiers, Information Sciences, vol.260, pp.120-148, 2014.
DOI : 10.1016/j.ins.2013.10.007

D. J. Foley, A. Van-dam, K. S. Feiner, and F. J. Hughes, Computer Graphics: Principles and Practice, 1992.

M. Fontana, F. Giannini, and M. Meirana, A Free Form Feature Taxonomy, Eurographics'99, pp.107-118, 1999.
DOI : 10.1111/1467-8659.00332

M. Fontana, F. Giannini, and M. Meirana, FREE FORM FEATURES FOR AESTHETIC DESIGN, International Journal of Shape Modeling, vol.06, issue.02, pp.273-302, 2000.
DOI : 10.1142/S0218654300000168

J. Furnkranz, E. Hullermeier, M. Loza, E. Brinker, and K. , Multilabel classification via calibrated label ranking, Machine Learning, pp.133-153, 2008.
DOI : 10.1007/s10994-008-5064-8

B. Ganster and R. Klein, An integrated framework for procedural modeling, Proceedings of the 23rd Spring Conference on Computer Graphics, SCCG '07, pp.150-157, 2007.
DOI : 10.1145/2614348.2614366

R. S. Garner, WEKA: The Waikato Environment for Knowledge Analysis, 1999.

J. H. George and P. Langley, Estimating Continuous Distributions in Bayesian Classifiers, 11th Conference on Uncertainty in Artificial Intelligence, 1995.

C. Geuzuaine, E. Marchandise, and J. Remacle, An introduction to Geometrical Modelling and Mesh, 1999.

E. E. Ghiselli, Theory of Psychological Measurement, 1964.

F. Giannini and M. Monti, An innovative approach to the aesthetic design, Common Ground , Design Research Society, International Conference, 2002.

F. Giannini and M. Monti, CAD Tools Based on Aesthetic Properties, Eurographics Italian Chapter Milano: Facolta' del Design -Politechico di Milano, 2002.

F. Giannini and M. Monti, Design intent-oriented modelling tools for aesthetic design, Journal of WSCG, vol.11, issue.1, 2003.

F. Giannini and M. Monti, A survey of tools for understanding and exploiting the link between shape and emotion in product design, 2010.

F. Giannini and M. Monti, A survey of tools for understending and exploiting the link between shape and emotion in product design, Proceedings of the TMCE 2010, 2010.

F. Giannini, E. Montani, M. Monti, and J. Pernot, Semantic Evaluation and Deformation of Curves Based on Aesthetic Criteria, Computer-Aided Design and Applications, vol.3, issue.2, pp.449-464, 2011.
DOI : 10.1016/j.compind.2008.03.004

URL : https://hal.archives-ouvertes.fr/hal-01404152

F. Giannini, M. Monti, and G. Podehl, Styling Properties and Features in Computer Aided Industrial Design, Computer-Aided Design and Applications, vol.17, issue.3, pp.1-4, 2004.
DOI : 10.1080/16864360.2004.10738273

F. Giannini, M. Monti, and G. Podehl, Aesthetic-driven tools for industrial design, Journal of Engineering Design, vol.5, issue.3, pp.193-215, 2006.
DOI : 10.1109/38.219453

F. Giannini, M. Monti, and G. Podehl, Aesthetic-driven tools for industrial design, Journal of Engineering Design, vol.5, issue.3, pp.193-215, 2006.
DOI : 10.1109/38.219453

F. Giannini, M. Monti, and G. Podehl, Aesthetic-driven tools for industrial design, Journal of Engineering Design, vol.5, issue.3, pp.193-215, 2012.
DOI : 10.1109/38.219453

F. Giannini, M. Monti, J. Pelletier, and J. Pernot, A Survey to Evaluate how non Designers Perceive Aesthetic Properties of Styling Features, Computer-Aided Design and Applications, vol.10, issue.1, pp.129-138, 2013.
DOI : 10.3722/cadaps.2011

S. Godbole and S. Sarawagi, Discriminative Methods for Multi-labeled Classification, Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2004.
DOI : 10.1007/978-3-540-24775-3_5

S. Guillet and J. Léon, Parametrically deformed free-form surfaces as part of a variational model, Computer-Aided Design, vol.30, issue.8, pp.621-630, 1998.
DOI : 10.1016/S0010-4485(98)00019-0

L. D. Gupta, A. K. Malviya, and S. Singh, Performance Analysis of Classification Tree Learning Algorithms, International Journal of Computer Applications, vol.55, issue.6, pp.39-44, 2012.
DOI : 10.5120/8762-2680

A. M. Hall, Correlation-based Feature Selection for Machine Learning, 1999.

A. M. Hall and A. L. Smith, Feature Selection for Machine Learning: Comparing a Correlation-based Filter Approach to the Wrapper, Proceedings of the Twelfth International FLAIRS Conference, 1999.

J. D. Hand and K. Yu, Idiot's Bayes -Not So Stupid After All?, International Statistical Review, vol.69, issue.3, pp.385-398, 2001.

T. Harada, N. Mori, and K. Sugiyama, Curves' physical characteristics and self-affine properties, Bulletin of Japonese Society for the Science of Design, vol.42, issue.3, pp.30-40, 1995.

M. C. Hoffmann, Geometric and Solid Modeling: An Introduction, 1989.

M. C. Hoffmann, Implicit curves and surfaces in CAGD, IEEE Computer Graphics and Applications, vol.13, issue.1, pp.79-88, 1993.
DOI : 10.1109/38.180121

M. C. Hoffmann and R. Joan-arinyo, Erep: An editable, high-level representation for geometric design and analysis, Geometric Modeling for Product Realization, pp.129-164, 1993.

M. C. Hoffmann and R. Joan-arinyo, Symbolic Constraints in Constructive Geometric Constraint Solving, Journal of Symbolic Computation, vol.23, issue.2-3, pp.287-299, 1997.
DOI : 10.1006/jsco.1996.0089

M. C. Hoffmann and R. Joan-arinyo, On user-defined features, Computer-Aided Design, vol.30, issue.5, pp.321-332, 1998.
DOI : 10.1016/S0010-4485(97)00048-1

M. C. Hoffmann and G. Vanecek, Fundamental Techniques for Geometric and Solid Modeling, Control and Dynamic Systems, vol.48, pp.101-159, 1991.
DOI : 10.1016/B978-0-12-012748-1.50009-4

S. Hou and K. Ramani, A Probability-Based Unified 3D Shape Search, European Commission International Conference on Semantic and Digital Media Technologies, Lecture notes in computer science, 2006.
DOI : 10.1007/11930334_10

S. Hou, K. Lou, and K. Romani, SVM-based Semantic Clustering and Retrieval of a 3D Model Database, Computer-Aided Design and Applications, vol.14, issue.5, pp.1-4, 2005.
DOI : 10.1080/16864360.2005.10738363

Y. Hsu, 11 25) Optimal Design Laboratory, 2010.

E. Hullermeier, J. Furnkranz, C. Weiwei, and K. Brinker, Label ranking by learning pairwise preferences, Artificial Intelligence, vol.172, issue.16-17, pp.16-17, 2008.
DOI : 10.1016/j.artint.2008.08.002

Y. C. Ip and C. W. Regli, Content-Based Classification of CAD Models with Supervised Learning, Computer-Aided Design and Applications, vol.12, issue.5, pp.609-617, 2005.
DOI : 10.1080/16864360.2005.10738325

Y. C. Ip, C. W. Regli, L. Sieger, and A. Shokoufandeh, Automated learning of model classifications, Proceedings of the eighth ACM symposium on Solid modeling and applications , SM '03, 2003.
DOI : 10.1145/781606.781659

A. Jamain and J. D. Hand, The Naive Bayes Mystery: A classification detective story, Pattern Recognition Letters, vol.26, issue.11, pp.1752-1760, 2005.
DOI : 10.1016/j.patrec.2005.02.001

S. Jeschke, H. Birkholz, and H. Schmann, A Procedural Model for Interactive Animation of Breaking Ocean Waves. WSCG POSTERS, 2003.

K. Joy, http://graphics.cs.ucdavis Récupéré sur Institute for Data Analysis and Visualization UC Davis: http://graphics, 2012.

I. Kanaya, Y. Nakano, and K. Sato, Classification of Aesthetic Curves and Surfaces for Industrial Design, Design Discourse, vol.2, issue.4, 2007.

K. Kira and A. L. Rendell, A Practical Approach to Feature Selection, Machine Learning: Proceedings of the Ninth International Conference, 1992.
DOI : 10.1016/B978-1-55860-247-2.50037-1

R. Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection, International Joint Conference on Artificial Intelligence, 1995.

R. Kohavi, Wrappers for Performance Enhancement and Oblivious Decision Graphs, 1995.

R. Kohavi and H. G. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X

R. Kohavi and D. Sommerfield, Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology, p.95, 1995.

D. Koller and M. Sahami, Toward Optimal Feature Selection, Machine Learning: Proceedings of the Thirteenth International Conference, 1996.

L. Kotthoff, P. I. Gent, and I. Miguel, An Evaluation of Machine Learning in Algorithm Selection for Search Problems, 2010.

R. Krakovsky and R. Forgac, Neural network approach to multidimensional data classification via clustering, 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics, 2011.
DOI : 10.1109/SISY.2011.6034316

L. Greca and R. , Approche déclarative de la modélisation de surfaces, 2005.

H. Laga, 3D Shape Classification and Retrieval Using Heterogenous Features and Supervised Learning, Machine Learning, pp.305-324, 2009.

D. A. Lattner, A. Miene, and O. Herzog, A Combination of Machine Learning and Image Processing Technologies for the Classification of Image Regions, Adaptive Multimedia Retrieval, pp.185-199, 2004.
DOI : 10.1007/978-3-540-25981-7_13

M. Lee, Using support vector machine with a hybrid feature selection method to the stock trend prediction, Expert Systems with Applications, vol.36, issue.8, pp.10896-10904, 2009.
DOI : 10.1016/j.eswa.2009.02.038

S. Lemm, B. Blankertz, T. Dickhaus, and K. Müller, Introduction to machine learning for brain imaging, NeuroImage, vol.56, issue.2, pp.387-399, 2011.
DOI : 10.1016/j.neuroimage.2010.11.004

M. Lesot, C. Bouchard, . Detyniecki, and J. Omhover, Product shape and emotional design ? an application to perfume bottle, International Conference on Kansei Engineering and Emotion Research, 2010.

H. Liu and L. Yu, Toward Integrating Feature Selection Algorithms for Classification and Clustering, Knowledge and Data Engineering, vol.17, issue.4, pp.491-502, 2005.

X. Lu, P. Suryanarayan, B. R. Adams, J. Li, G. M. Newman et al., On shape and the computability of emotions, Proceedings of the 20th ACM international conference on Multimedia, MM '12, 2012.
DOI : 10.1145/2393347.2393384

M. Lucas, D. Martin, M. Philippe, and D. Plémenos, Le projet ExploFormes, quelques pas vers la modélisation déclarative de formes, Bigre, vol.67, pp.35-49, 1990.

L. Luo and X. Chen, Integrating piecewise linear representation and weighted support vector machine for stock trading signal prediction, Applied Soft Computing, vol.13, issue.2, pp.806-816, 2013.
DOI : 10.1016/j.asoc.2012.10.026

R. Maculet and M. Daniel, Conception, mod??lisation g??om??trique et contraintes en CAO : une synth??se, Revue d'intelligence artificielle, vol.18, issue.5-6, pp.5-6, 2004.
DOI : 10.3166/ria.18.619-645

M. Maddouri and M. Elloumi, A data mining approach based on machine learning techniques to classify biological sequences. Knowledge-Based Systems, pp.217-223, 2002.

G. Madjarov, D. Kocev, D. Gjorgjevikj, and S. Dzeroski, An extensive experimental comparison of methods for multi-label learning, Pattern Recognition, vol.45, issue.9, pp.3084-3104, 2012.
DOI : 10.1016/j.patcog.2012.03.004

M. Maahl, N. Guid, ?. Oloo?ek, and M. Hoat, EEtesioos of sseep sufaae constructions, Computers & Graphics, vol.999, issue.206, pp.893-903

D. Martin and P. Martin, An expert system for polyhedral modeling, 1988.

V. D. Meiden, A. Hilderick, and F. W. Bronsvoort, Solving topological constraints for declarative families of objects, Computer-Aided Design, pp.652-662, 2007.

N. Michael, Artificial Intelligence -A Guide to Intelligent Systems, 2002.

P. Mitchell-guthrie, 08 22) Subconscious Musings -Advanced analytics from Research Drive to the world Récupéré sur SAS: http://blogs.sas.com/content/subconsciousmusingslooking-backwards- looking-forwards-sas-data-mining-and-machine-learning, 2014.

T. K. Miura and U. G. Rudrusamy, Aesthetic Curves and Surfaces in Computer Aided Geometric Design, International Journal of Automation Technology, vol.8, issue.3, pp.304-316, 2014.

T. K. Miura, J. Sone, A. Yamashita, and T. Kaneko, Derivation of a general formula of aestetic curves, proceedings of the Eighth International Conference on Humans and Computers, 2005.

E. M. Mortenson, Geometric Modeling, 1995.

G. A. Motaal, N. El-gayar, and F. N. Osman, Different Regions Identification in Composite Strain-Encoded (C-SENC) Images Using Machine Learning Techniques, Artificial Neural Networks in Pattern Recognition, pp.231-240, 2010.
DOI : 10.1007/978-3-642-12159-3_21

P. Muller, P. Wonka, S. Haegler, A. Ulmer, and L. Van-gool, Procedural modeling of buildings, ACM Transactions on Graphics, pp.614-623, 2006.

M. Nagamachi, Kansei/Affective Engineering, Boca Raton, vol.20101242, 2011.
DOI : 10.1201/EBK1439821336

R. G. Negri, L. V. Dutra, and S. J. Sant-anna, An innovative support vector machine based method for contextual image classification, ISPRS Journal of Photogrammetry and Remote Sensing, vol.87, pp.241-248, 2014.
DOI : 10.1016/j.isprsjprs.2013.11.004

L. Ni, Z. Ni, and Y. Gao, Stock trend prediction based on fractal feature selection and support vector machine, Expert Systems with Applications, vol.38, issue.5, pp.5569-5576, 2011.
DOI : 10.1016/j.eswa.2010.10.079

S. Park and F. Johannes, Efficient Pairwise Classification, Proceedings of the 18th European Conference on Machine Learning, 2007.
DOI : 10.1007/978-3-540-74958-5_65

M. N. Patrikalakis, Récupéré sur Massachusetts Institute of Technology: http://ocw.mit.edu/courses/mechanical-engineering/2-158j- computational-geometry-spring-2003/lecture-notes, 2003.

J. Pernot, Fully Free Form Deformation Features for Aesthetic and Engineering Design, 2004.

J. Pernot, Q. Quao, and P. Veron, Constraints Automatic Relaxation to Design Products with Fully Free Form Features, Advances in Integrated Design and Manufacturing in Mechanical Engineering II, pp.145-160, 2007.
DOI : 10.1007/978-1-4020-6761-7_10

A. Petrov, J. Pernot, P. Veron, F. Giannini, and B. Falcidieno, Aesthetic-oriented classification of 2D free-form curves, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01408794

L. Piegl, On NURBS: a survey, IEEE Computer Graphics and Applications, vol.11, issue.1, pp.55-71, 1991.
DOI : 10.1109/38.67702

L. Piegl and W. Tiller, The NURBS Book, 1997.

D. Plamenos, La modélisation déclarative en synthèse d'images, tendences et perspectives, pp.94-97, 1994.

G. Podehl, Terms and Measures for Styling Properties. International Design Conference -Design, 2002.

G. Qi, X. Hua, Y. Rui, J. Tang, and H. Zhang, Two-Dimensional Multi-Label Active Learning with An Efficient Online Adaptation Model for Image Classification, IEEE Transaction on Pattern Analysis and Machine Intelligence, issue.10, pp.31-1880, 2009.

R. Quinlan, C4.5: Programs for Machine Learning, 1993.

J. Read, PhD thesis: Scalable Multi-label Classification, 2010.

J. Read, L. Martino, and D. Luengo, Efficient monte carlo methods for multi-dimensional learning with classifier chains, Pattern Recognition, vol.47, issue.3, pp.1535-1546, 2014.
DOI : 10.1016/j.patcog.2013.10.006

J. Read, B. Pfahringer, and G. F. Holmes, Classifier Chains for Multi-label Classification, Machine Learning, pp.333-359, 2011.

Y. Ren, Design Preference Elicitation, Identification and Estimation, 2012.

S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 1995.

L. Sajn and M. Kukar, Image processing and machine learning for fully automated probabilistic evaluation of medical images, Computer Methods and Programs in Biomedicine, vol.104, issue.3, pp.75-86, 2011.
DOI : 10.1016/j.cmpb.2010.06.021

F. Schwenker and E. Trentin, Pattern classification and clustering: A review of partially supervised learning approaches, Pattern Recognition Letters, vol.37, issue.1, pp.4-14, 2014.
DOI : 10.1016/j.patrec.2013.10.017

W. T. Sederberg, C. D. Anderson, and N. R. Goldman, Implicit Representation of Parametric Curves and Surfaces, Computer Vision, Graphics, and Image Processing, pp.72-84, 1984.

A. Shhab, G. Guo, and D. Neagu, A Study on Applications of Machine Learning Techniques in Data Mining, 2001.

P. Simon, Too Big to Ignore: The Business Case for Big Data, 2013.
DOI : 10.1002/9781119204039

V. Singh and N. Gu, Towards an integrated generative design framework, Design Studies, vol.33, issue.2, pp.185-207, 2012.
DOI : 10.1016/j.destud.2011.06.001

M. R. Smelik, T. Tutenel, R. Bidarra, and B. Benes, A Survey on Procedural Modelling for Virtual Worlds, Computer Graphics Forum, vol.32, issue.4, pp.31-50, 2014.
DOI : 10.1111/cgf.12276

R. Srinivasana, C. Wang, W. K. Ho, and K. W. Lim, Neural network systems for multi-dimensional temporal pattern classification, Computers & Chemical Engineering, vol.29, issue.5, pp.965-981, 2005.
DOI : 10.1016/j.compchemeng.2004.09.026

P. W. Stevens, J. G. Myers, and L. L. Constantine, Structured design, IBM Systems Journal, vol.13, issue.2, pp.115-139, 1974.
DOI : 10.1147/sj.132.0115

G. Stiny and J. Gips, Shape Grammars and the Generative Spesification of Painting and Sculptures . International Federation for Information Processing, 1971.

J. Su and H. Zhang, Full Bayesian network classifiers, Proceedings of the 23rd international conference on Machine learning , ICML '06, 2006.
DOI : 10.1145/1143844.1143957

M. R. Summmers and S. Wang, Machine learning in radiology, Medical Image Analysis, vol.16, pp.933-951, 2012.

P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2006.

R. Tiwari and P. M. Singh, Correlation-based Attribute Selection using Genetic Algorithm, International Journal of Computer Applications, vol.4, issue.8, pp.975-8887, 2010.
DOI : 10.5120/847-1182

K. Trohidis, G. Tsoumakas, G. Kalliris, and I. Vlahavas, Multi-label classification of music by emotion, The Proceedings of the 9th International Conference on Music Information Retrieval, 2008.
DOI : 10.1007/s10994-008-5077-3

G. Tsoumakas and I. Katakis, Multi-Label Classification, International Journal of Data Warehousing and Mining, vol.3, issue.3, pp.1-13, 2007.
DOI : 10.4018/jdwm.2007070101

G. Tsoumakas and I. Vlahavas, Random k-Labelsets: An Ensemble Method for Multilabel Classification, 18th European Conference on Machine Learning, 2007.
DOI : 10.1007/978-3-540-74958-5_38

C. Unsalan and A. Ercil, Conversions between Parametric and Implicit Forms Using Polar/Spherical Coordinate Representations, Computer Vision and Image Understanding, vol.81, issue.1, pp.1-25, 2001.
DOI : 10.1006/cviu.2000.0881

V. Vladimir, The nature of statistical learning theory, 1995.

U. O. Waikato, (s.d.). WEKA. Consulté le 05 31, 2015.

B. A. Walid and B. Yannou, Unsupervised Beyesian Models to Carry out Perceptual Evaluation in a Design Process, International Conference en Engineering Design, p.7, 2007.

X. Wang, D. Nie, and B. Lu, Emotional state classification from EEG data using machine learning approach, Neurocomputing, vol.129, pp.94-106, 2014.
DOI : 10.1016/j.neucom.2013.06.046

S. P. Wasan, M. Uttamchandani, M. S. Moochhala, and V. B. Yap, Application of statistics and machine learning for risk stratification of heritable cardiac arrhythmias, Expert Systems with Applications, vol.40, issue.7, pp.2476-2486, 2013.
DOI : 10.1016/j.eswa.2012.10.054

B. Watson, P. Müller, P. Wonka, C. Sexton, O. Veryovka et al., Procedural Urban Modeling in Practice, IEEE Computer Graphics and Applications, vol.28, issue.3, pp.18-26, 2008.
DOI : 10.1109/MCG.2008.58

H. I. Witten, E. Frank, and A. M. Hall, Data Mining -Practical Machine Learning Tools and Techniques, 2011.

D. Xu and H. Li, 3D Shape Retrieval Integrated with Classification Information, Fourth International Conference on Image and Graphics (ICIG 2007), 2007.
DOI : 10.1109/ICIG.2007.13

K. Xu, G. V. Kim, Q. Huang, and E. Kalogerakis, Data-Driven Shape Analysis and Processing, Computer Graphics Forum, pp.1-27, 2015.

X. Xu, Integrating Advanced Computer-Aided Design, Manufacturing, and Numerical Control: Principles and Implementations, 2009.

N. Yoshida and T. Saito, Quasi-Aesthetic Curves in Rational Cubic B??zier Forms, Computer-Aided Design and Applications, vol.22, issue.9, pp.1-4, 2007.
DOI : 10.1080/16864360.2007.10738567

H. L. You, J. Chang, X. Yang, and J. J. Zhang, Solid modelling based on sixth order partial differential equations, Computer-Aided Design, vol.43, issue.6, pp.720-729, 2011.
DOI : 10.1016/j.cad.2011.01.021

H. J. Zaragoza, L. E. Sucar, F. E. Morales, C. Bielza, and P. Larranaga, Bayesian Chain Classifiers for Multidimensional Classification, Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, 2011.

M. Zhang and Z. Zhou, Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization, IEEE Transactions on Knowledge and Data Engineering, issue.10, pp.18-1338, 2006.

M. Zhang and Z. Zhou, ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognition, vol.40, issue.7, pp.2038-2048, 2007.
DOI : 10.1016/j.patcog.2006.12.019

A. Appendix, List of Specifications of the ML algorithms, p.173