M. Abba, Y. Hu, and H. Sun, Gene expression signature of estrogen receptor ? status in breast cancer, BMC Genomics, vol.6, pp.74-81, 2005.

S. Abe and R. Thawonmas, A fuzzy classifier with ellipsoidal regions, IEEE Transactions on Fuzzy Systems, vol.5, issue.3, pp.358-368, 1997.
DOI : 10.1109/91.618273

J. C. Aguado and J. Aguilar-martin, A mixed qualitative-quantitative self-learning classification technique applied to diagnosis, The Thirteenth Int'l Workshop on Qualitative Reasoning Chris Price, pp.124-128, 1999.

J. Aguilar and R. Lopez-de-mantaras, The process of classification and learning the meaning of linguistic descriptors of concepts, Approximate reasoning in decision analysis, pp.165-175, 1982.

D. W. Aha, INCREMENTAL, INSTANCE-BASED LEARNING OF INDEPENDENT AND GRADED CONCEPT DESCRIPTIONS, Proced. of the 6 th int'l Mach. Learning Workshop, pp.387-391, 1989.
DOI : 10.1016/B978-1-55860-036-2.50098-9

D. W. Aha, Tolerating noisy, irrelevant and novel attributes in instance based learning algorithms, Int. Man-Machine Studies 36, pp.267-287, 1992.

A. A. Alizadeh, M. B. Eisen, and R. E. Davis, Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling, Nature, vol.303, issue.6769, pp.403-503, 2000.
DOI : 10.1038/35000501

R. Andrews, R. Mah, S. Jeffery, M. Guerrero, R. Papasin et al., The NASA smart probe project for real-time multiple microsensor tissue recognition, Int'l Congress Series, pp.547-554, 1256.

K. Antman and S. Shea, Screening Mammography Under Age 50, JAMA, vol.281, issue.16, pp.281-1470, 1999.
DOI : 10.1001/jama.281.16.1470

M. Ayers, W. F. Symmans, and J. Stec, Gene Expression Profiles Predict Complete Pathologic Response to Neoadjuvant Paclitaxel and Fluorouracil, Doxorubicin, and Cyclophosphamide Chemotherapy in Breast Cancer, Journal of Clinical Oncology, vol.22, issue.12, pp.2284-2293, 2004.
DOI : 10.1200/JCO.2004.05.166

P. Baldi and S. Brunak, Bioinformatics: The machine learning approach, 2001.

A. Baraldi and P. Blonda, A survey of fuzzy clustering algorithms for pattern recognition. I, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.29, issue.6, pp.778-785, 1999.
DOI : 10.1109/3477.809032

V. J. Bardou, G. Arpino, and R. M. Alledge, Progesterone Receptor Status Significantly Improves Outcome Prediction Over Estrogen Receptor Status Alone for Adjuvant Endocrine Therapy in Two Large Breast Cancer Databases, Journal of Clinical Oncology, vol.21, issue.10, p.1973
DOI : 10.1200/JCO.2003.09.099

V. V. Belle and B. V. Calster, Brouckaert Receptor and HER2 Improves the Nottingham Prognostic Index Up to 5 Years After Breast Cancer Diagnosis, J of Clin Onco

R. Bellman, Adaptive control processes: A Guided Tour
DOI : 10.1515/9781400874668

F. Bertucci and V. Nasser, Granjeaud primary breast cancer correlate with survival, Human Molecular Genetics, vol.11, issue.8

J. C. Bezdec, Fuzzy Pattern Recognition, 1981.
DOI : 10.1002/047134608X.W3505

A. Bhattacharjee, W. Richards, and J. Staunton, mRNA expression profiling reveals distinct adenocarcinoma subclasses, pp.98-13790, 2001.

L. Billard, Some Analyses of Interval Data, Journal of Computing and Information Technology, vol.16, issue.4, p.225
DOI : 10.2498/cit.1001390

H. J. Bloom and W. W. Richardson, Histological grading and prognosis in 1409 cases of which 359 have been followed for 15 years, Br J Cancer, issue.11, p.359

H. H. Bock and E. Diday, Analysis of Symbolic Data, Exploratory Methods for Extracting Statistical Information from Complex Data

P. S. Bradley and O. L. Mangasarian, Feature selection via concave minimization and support vector machines, Mach. Learn. Proc. of the 15 pp, pp.82-90, 1998.

G. Alledge and R. M. , Progestrone receptor status significantly improves outcome prediction over estrogen receptor status alone for adjuvant endocrine therapy in two large breast cancer database, J Clin Oncol, vol.21, pp.1973-1979, 2003.

O. Brouckaert, Qualitative Assessment of the Progesterone Receptor and HER2 Improves the Nottingham Prognostic Index Up to 5 Years After, Breast J of Clin Onco, vol.28, issue.27, pp.4129-4134, 2010.

S. Granjeaud, Gene expression profiles of poor ast cancer correlate with survival, Oxford Journals Life Sciences, vol.11, issue.8, pp.863-872, 2002.

W. Richards and J. Staunton, Pattern Recognition with Fuzzy Objective Function Algorithms Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses, Proc. Nat'l Acad. Sc. 13795, 2001.

L. Billard, Some Analyses of Interval Data, Journal of Computing and Information Technology, vol.16, issue.4, p.225
DOI : 10.2498/cit.1001390

P. Inza, I. Sierra, and B. , Gene selection for cancer classification using pper approaches, Int'l J of Pattern Recognition and Artificial Intelligence

H. J. Bloom and W. W. Richardson, Histological grading and prognosis in breast cancer; a study of 1409 cases of which 359 have been followed for 15 years, Br J Cancer, issue.11, p.359

H. H. Bock and E. Diday, Analysis of Symbolic Data, Exploratory Methods for Extracting Statistical Information from Complex Data, 2000.

P. S. Bradley and O. L. Mangasarian, Feature selection via concave minimization and support, Mach. Learn. Proc. of the 15 th Intl. Conf. Mach. Learn

H. Olshen, R. A. Stone, and C. J. , Classification and regression trees, 1984.

D. N. Stec and J. , Global gene expression changes during therapy for human breast cancer, Progestrone receptor status significantly References 163, pp.461-468, 2002.

A. Buness, M. Ruschhaupt, R. Kuner, and A. Tresch, Classification across gene expression microarray studies, BMC Bioinformatics, vol.10, issue.1, pp.410-453, 2009.
DOI : 10.1186/1471-2105-10-453

M. Buyse, S. Loi, L. Van-'t-veer, and G. Viale, Validation and Clinical Utility of a 70-Gene Prognostic Signature for Women With Node-Negative Breast Cancer, JNCI Journal of the National Cancer Institute, vol.98, issue.17, pp.98-1183, 2006.
DOI : 10.1093/jnci/djj329

K. Byrnes, S. White, and Q. Chu, High eIF4E, VEGF, and Microvessel Density in Stage I to III Breast Cancer, Annals of Surgery, vol.243, issue.5, pp.684-690, 2006.
DOI : 10.1097/01.sla.0000216770.23642.d8

Y. Cai, Y. Sun, J. Li, and S. Goodison, Online Feature Selection Algorithm with bayesian ? 1

Y. Cai, Y. Sun, Y. Cheng, J. Li, and S. Goodison, Fast Implementation of ? 1 Regularized Learning Algorithms Using Gradient Descent Methods, pp.862-871, 2010.

Y. Cai, L. Hedjazi, Y. Sun, and S. Goodison, Fast Implementation of ?1 Regularized Learning Algorithms Using Gradient Descent Methods, submitted to, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2010.

C. L. Carter, C. Allen, and D. E. Henson, Relation of tumor size, lymph node status, and survival in 24,740 breast cancer cases, Cancer, vol.45, issue.1, pp.181-187, 1989.
DOI : 10.1002/1097-0142(19890101)63:1<181::AID-CNCR2820630129>3.0.CO;2-H

J. C. Chang, E. C. Wooten, and A. Tsimelzon, Gene expression profiling predicts therapeutic response to docetaxel (Taxotere) in breast cancer patients, Lancet, vol.362, pp.280-287, 2003.

J. C. Chang, S. G. Hilsenbeck, and S. A. Fuqua, Genomic approaches in the management and treatment of breast cancer, British Journal of Cancer, vol.17, issue.4, pp.618-624, 2005.
DOI : 10.1056/NEJMoa021967

R. Chang, W. Wu, and W. K. Moon, Support Vector Machines for Diagnosis of Breast Tumors on US Images, Academic Radiology, vol.10, issue.2, pp.189-197, 2003.
DOI : 10.1016/S1076-6332(03)80044-2

O. Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, vol.6, issue.5, pp.1155-1178, 2007.
DOI : 10.1198/106186005X25619

S. Chen, A weighted fuzzy reasoning algorithm for medical diagnosis, decision support systems, pp.37-43, 1994.

C. Cheng, Y. Lin, and M. Tsai, SCUBE2 Suppresses Breast Tumor Cell Proliferation and Confers a Favorable Prognosis in Invasive Breast Cancer, Cancer Research, vol.69, issue.8, pp.3634-3641, 2009.
DOI : 10.1158/0008-5472.CAN-08-3615

Y. Cheng and G. M. Church, Biclustering of expression data, Proc Int Conf Intell Syst Mol Biol, vol.8, pp.93-103, 2000.

K. L. Cheung, C. R. Graves, and J. F. Robertson, Tumour marker measurements in the diagnosis and monitoring of breast cancer, Cancer Treatment Reviews, vol.26, issue.2, pp.91-102, 2000.
DOI : 10.1053/ctrv.1999.0151

S. L. Chiu, H. Dubois, R. Prade, and . Yager, Extracting fuzzy rules from data for function approximation and pattern classification, In Fuzzy Information Engineering: A guided Tour of Applications, 1997.

D. V. Cicchetti, Neural networks and diagnosis in the clinical laboratory: state of the art, Clin Chem, vol.38, pp.9-10, 1992.

S. J. Cleator, A. Makris, S. E. Ashley, R. Lal, and T. J. Powles, Good clinical response of breast cancers to neoadjuvant chemoendocrine therapy is associated with improved overall survival, Annals of Oncology, vol.16, issue.2, pp.267-272, 2004.
DOI : 10.1093/annonc/mdi049

A. J. Cochran, Prediction of Outcome for Patients with Cutaneous Melanoma, Pigment Cell Research, vol.220, issue.3, pp.162-167, 1997.
DOI : 10.1016/0140-6736(91)92100-G

M. Colozza, E. Azambuja, and F. Cardoso, Proliferative markers as prognostic and predictive tools in early breast cancer: where are we now?, Annals of Oncology, vol.16, issue.11, pp.1723-1739, 2005.
DOI : 10.1093/annonc/mdi352

J. P. Costella, A simple alternative to Kaplan?Meier for survival curves, 2010.

D. G. Covell, A. Wallqvist, A. A. Rabow, and N. Thanki, Molecular Classification of Cancer: Unsupervised Self-Organizing Map Analysis of Gene Expression Microarray Data, Mol Cancer Ther, vol.2, pp.317-332, 2003.

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Transactions on Information Theory, vol.13, issue.1, pp.21-27, 1967.
DOI : 10.1109/TIT.1967.1053964

V. V. Cross and T. A. Sudkamp, Similarity and Compatibility in Fuzzy Set Theory: assessment and Applications, Physica-Verlag, 2002.
DOI : 10.1007/978-3-7908-1793-5

J. A. Cruz and D. S. Wishart, Application of Machine Learning in Cancer Prediction and Prognosis, Cancer Informatics, vol.2, pp.59-77, 2006.

S. Das, Filters, Wrappers, and a boosting-based Hybrid for feature selection, Proc. 18 th Int'l Conf. Machine Learning, pp.74-81, 2001.

M. Dash and H. Liu, Consistency-based search in feature selection, Artificial Intelligence, vol.151, issue.1-2, pp.155-176, 2003.
DOI : 10.1016/S0004-3702(03)00079-1

D. Carvalho, F. A. , D. Souza, and R. M. , Unsupervised pattern recognition models for mixed feature-type symbolic data, Pattern Recognition Letters, vol.31, issue.5, pp.31-430, 2010.
DOI : 10.1016/j.patrec.2009.11.007

S. Deepa and I. Claudine, Utilizing Prognostic and Predictive Factors in Breast Cancer, Current Treatment Options in Oncology Current Science Inc, vol.6, pp.147-159, 2005.

D. Delen, G. Walker, and A. Kadam, Predicting breast cancer survivability: a comparison of three data mining methods, Artificial Intelligence in Medicine, vol.34, issue.2, pp.113-127, 2005.
DOI : 10.1016/j.artmed.2004.07.002

D. Souto, M. Costa, I. , D. Araujo, and D. , Clustering cancer gene expression data: a comparative study, BMC Bioinformatics, vol.9, issue.1, p.497, 2008.
DOI : 10.1186/1471-2105-9-497

M. Dettling, E. Gabrielson, and G. Parmigiani, Searching for di®erentially expressed gene combinations, Genome Biology, vol.6, issue.10, p.88, 2005.
DOI : 10.1186/gb-2005-6-10-r88

S. G. Diab, G. M. Clark, and C. K. Osborne, Tumor Characteristics and Clinical Outcome of Tubular and Mucinous Breast Carcinomas, Journal of Clinical Oncology, vol.17, issue.5, pp.1442-1448, 1999.
DOI : 10.1200/JCO.1999.17.5.1442

T. G. Dietterich, Machine Learning Research: Four Current Directions, AI Magazine, vol.18, issue.4, pp.97-136, 1997.

D. Dubois, H. Prade, and C. Testemale, Weighted fuzzy pattern matching, Fuzzy Sets and Systems, pp.313-331, 1988.

D. Dubois and H. Prade, The three semantics of fuzzy sets, Fuzzy Sets and Syst, pp.141-150, 1997.

J. Duchi, S. Shalev-shwartz, Y. Singer, and C. T. , Efficient projections onto the L1-ball for learning in high dimensions, Proc. 25th Intl. Conf. Mach. Learn, pp.272-279, 2008.

S. Dudoit, J. Fridlyand, and T. P. Speed, Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, Journal of the American Statistical Association, vol.97, issue.457, pp.77-87, 2002.
DOI : 10.1198/016214502753479248

M. J. Duffy and C. Duggan, The urokinase plasminogen activator system: a rich source of tumour markers for the individualised management of patients with cancer, Clinical Biochemistry, vol.37, issue.7, pp.541-548, 2004.
DOI : 10.1016/j.clinbiochem.2004.05.013

D. J. Duggan, M. Bittner, and Y. Chen, Expression profiling using cDNA microarrays, Nature Genetics, vol.21, pp.10-37, 1999.
DOI : 10.1038/4434

J. G. Dy and C. E. Brodley, Feature Selection for unsupervised learning, pp.845-889, 2004.

P. Eifel, J. A. Axelson, and J. Costa, National institutes of health consensus development conference statement: Adjuvant therapy for breast cancer, Journal of National Cancer Institute, issue.13, pp.93-979, 2001.

Y. G. Evtushenkjo and V. G. Zhadan, Space-Transformation Technique: The State of the Art, Nonlinear Optimization and Applications, pp.101-123, 1996.
DOI : 10.1007/978-1-4899-0289-4_8

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

L. Faybusovich, Dynamical Systems which Solve Optimization Problems with Linear Constraints, IMA Journal of Mathematical Control and Information, vol.8, issue.2, pp.135-149, 1991.
DOI : 10.1093/imamci/8.2.135

M. Filippone, F. Camastra, F. Masulli, and S. Rovetta, A survey of kernel and spectral methods for clustering, Pattern recognition, pp.176-190, 2008.

B. Fisher, J. Bryant, and N. Wolmark, Effect of preoperative chemotherapy on the outcome of women with operable breast cancer., Journal of Clinical Oncology, vol.16, issue.8, pp.2672-2685, 1998.
DOI : 10.1200/JCO.1998.16.8.2672

R. Fletcher, Practical methods of optimization, 1997.
DOI : 10.1002/9781118723203

Y. Freund and R. E. Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, Journal of Computer and System Sciences, vol.55, issue.1, pp.119-139, 1997.
DOI : 10.1006/jcss.1997.1504

T. Fujita, H. Doihara, and K. Kawasaki, PTEN activity could be a predictive marker of trastuzumab efficacy in the treatment of ErbB2-overexpressing breast cancer, British Journal of Cancer, vol.57, issue.2, pp.247-252, 2006.
DOI : 10.1038/sj.onc.1203972

K. Fukunaga, Introduction to statistical pattern recognition, 1990.

G. Fung and O. L. Mangasarian, A Feature Selection Newton Method for Support Vector Machine Classification, Computational Optimization and Applications, vol.28, issue.2, pp.185-202, 2004.
DOI : 10.1023/B:COAP.0000026884.66338.df

M. H. Galea, R. W. Blamey, C. E. Elston, and E. I. , The Nottingham prognostic index in primary breast cancer, Breast Cancer Research and Treatment, vol.74, issue.3, pp.207-219, 1992.
DOI : 10.1007/BF01840834

R. Gallardo-caballero, C. J. García-orellana, H. M. González-velasco, and M. Macías-macías, Independent Component Analysis Applied to Detection of Early Breast Cancer Signs, Proceedings of the 9th international work conference on Artificial neural networks, pp.988-995, 2007.
DOI : 10.1007/978-3-540-73007-1_119

O. Gevaert, D. Smet, F. Timmerman, D. Moreau, Y. De-moor et al., Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks, Bioinformatics, vol.22, issue.14, pp.22-184, 2006.
DOI : 10.1093/bioinformatics/btl230

R. Gilad-bachrach, A. Navot, and N. Tishby, Margin based feature selection - theory and algorithms, Twenty-first international conference on Machine learning , ICML '04, pp.43-50, 2004.
DOI : 10.1145/1015330.1015352

A. Goldhirsh, W. C. Wood, and R. D. Gelber, Meeting Highlights: Updated International Expert Consensus on the Primary Therapy of Early Breast Cancer, Journal of Clinical Oncology, vol.21, issue.17, pp.3357-3365, 2003.
DOI : 10.1200/JCO.2003.04.576

T. Golub, D. Slonim, and P. Tamayo, Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, Science, vol.286, issue.5439, pp.531-537, 1999.
DOI : 10.1126/science.286.5439.531

J. A. Gómez-ruiz, J. M. Jerez-aragonés, J. Muñoz-pérez, and E. Alba-conejo, A Neural Network Based Model for Prognosis of Early Breast Cancer, Applied Intelligence, vol.20, issue.3, pp.231-238, 2004.
DOI : 10.1023/B:APIN.0000021415.88365.c4

K. C. Gowda and E. Diday, Symbolic clustering using a new similarity measure, IEEE Transactions on Systems, Man, and Cybernetics, vol.22, issue.2, pp.368-378, 1992.
DOI : 10.1109/21.148412

K. Grammer, R. Gilad-bachrach, A. Navot, and N. Tishby, Margin analysis of the lvq algorithm, proceedings 17 th Int'l Conf. on Neural Information Processing Systems, 2002.

I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, Gene selection for cancer classification using support vector machines, Mach. Learning, vol.46, pp.1-3, 2002.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

I. Guyon, S. R. Gunn, A. Ben-hur, and G. Dror, Result analysis of the NIPS 2003 feature selection challenge, 17th Adv. Neu. Info. Proc. Sys, pp.545-552, 2005.

B. G. Haffty, P. Kornguth, D. Fisher, M. Beinfield, and C. Mckhann, Mammographically detected breast cancer. Results with conservative surgery and radiation therapy, Cancer, vol.320, issue.11, pp.2801-2804, 1991.
DOI : 10.1002/1097-0142(19910601)67:11<2801::AID-CNCR2820671115>3.0.CO;2-S

M. B. Haibe-kains, Identification and Assessment of Gene Signatures in Human Breast Cancer, Thesis, 2009.

M. B. Haibe-kains, C. Desmedt, and F. Rothé, A fuzzy gene expression-based computational approach improves breast cancer prognostication, Genome Biology, vol.11, issue.2, pp.11-18, 2010.
DOI : 10.1186/gb-2010-11-2-r18

M. A. Hall, Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning, Int. Conf. Mach. Learning ICML, pp.359-366, 2000.

M. Hayat, A, Methods of Cancer Diagnosis, Therapy and Prognosis: Breast Carcinoma, 2008.

L. Hedjazi, T. Kempowsky, L. Lann, M. V. Aguilar, and M. J. , Classification floue des données intervallaires : Application au pronostic du cancer, Actes du XVIem Rencontres de la société francophone de classification SFC'09, pp.165-168, 2009.

L. Hedjazi, J. Aguilar-martin, L. Lann, M. V. Kempowsky, and T. , Fuzzy mechanisms for unified reasoning about heterogeneous data

L. Hedjazi, T. Kempowsky, L. Lann, M. V. Aguilar-martin, and J. , Prognosis of breast cancer based on a fuzzy classification method, th Int'l Joint Conf Biomed Eng Syst and Tech, 1 st Int'l Conf. on Bioinformatics, pp.123-130, 2010.

L. Hedjazi, T. Kempowsky, L. Despenes, S. Elgue, L. Lann et al., Sensor placement and fault detection using an efficient fuzzy feature selection approach, 49 th IEEE Int, Conf. on Decision and Control CDC, pp.6827-6832, 2010.

L. Hedjazi, J. Aguilar-martin, L. Lann, M. V. Kempowsky, and T. , Towards a Unified Principle for Reasoning about Heterogeneous Data: A Fuzzy Logic Framework, Submitted for publication in, Fuzziness and Knowledge-Based Systems (Under revision), 2011.

L. Hedjazi, J. Aguilar-martin, L. Lann, and M. V. , Similarity-margin based feature selection for symbolic interval data, Pattern Recognition Letters, vol.32, issue.4, pp.578-585, 2011.
DOI : 10.1016/j.patrec.2010.11.018

L. Hedjazi, J. Aguilar-martin, L. Lann, M. V. Kempowsky, and T. , Membership-Margin based feature selection for Mixed-Type and High-Dimensional Data, 2011.

L. Hedjazi, T. Kempowsky, L. Lann, M. V. , F. Dalenc et al., Improved Breast Cancer Prognosis based on a Hybrid Marker Selection Approach, th Int'l Joint Conf Biomed Eng Syst and Tech 2 nd Int'l Conf. on Bioinformatics, pp.159-164, 2011.

S. Elgue, From chemical process diagnosis to cancer prognosis: an integrated approach for diagnosis and sensor/marker selection, 21st European Symposium on Computer Aided Process Engineering (ESCAPE-21), pp.1510-1514, 2011.

R. Horn and J. C. , Matrix analysis, 1985.

Q. H. Hu, Z. X. Xie, and D. R. Yu, Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation, Pattern Recognition, vol.40, issue.12, pp.3509-3521, 2007.
DOI : 10.1016/j.patcog.2007.03.017

Q. Hu, D. Yu, J. Liu, and C. Wu, Neighborhood rough set based heterogeneous feature subset selection, Information sciences, pp.3577-3594, 2008.
DOI : 10.1016/j.ins.2008.05.024

W. Huber, A. Von-heydebreck, H. Sültmann, A. Poustka, and M. Vingron, Variance stabilization applied to microarray data calibration and to the quantification of differential expression, Bioinformatics, vol.18, issue.Suppl 1, pp.96-104, 2002.
DOI : 10.1093/bioinformatics/18.suppl_1.S96

G. Hudelist, W. J. Köstler, and K. Czerwenka, Her-2/neu and EGFR tyrosine kinase activation predict the efficacy of trastuzumab-based therapy in patients with metastatic breast cancer, International Journal of Cancer, vol.63, issue.5, pp.1126-1134, 2005.
DOI : 10.1002/ijc.21492

I. Inza, P. Larrañaga, R. Blanco, and A. J. Cerrolaza, Filter versus wrapper gene selection approaches in DNA microarray domains, Artificial Intelligence in Medicine, vol.31, issue.2, pp.91-103, 2004.
DOI : 10.1016/j.artmed.2004.01.007

C. Isaza, T. Kempowsky, J. Aguilar, and A. Gauthier, Qualitative data Classification Using LAMDA and other Soft-Computer Methods, Recent Advances in Artificial Intelligence Research and Development, 2004.

H. Ishibuchi, K. Nozaki, and H. Tanaka, Distributed representation of fuzzy rules and its application to pattern classification, Fuzzy sets and syst, pp.21-32, 1992.

H. Ishibuchi and T. Nakashima, Effect of rule weights in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems, vol.9, issue.4, pp.506-515, 2001.
DOI : 10.1109/91.940964

H. Ishibuchi and T. Yamamoto, Rule weight specification in fuzzy rule-based classification systems, IEEE Transactions on Fuzzy Systems, vol.13, issue.4, pp.428-435, 2005.
DOI : 10.1109/TFUZZ.2004.841738

A. V. Ivshina, J. George, and O. Senko, Genetic Reclassification of Histologic Grade Delineates New Clinical Subtypes of Breast Cancer, Cancer Research, vol.66, issue.21, pp.10292-10301, 2006.
DOI : 10.1158/0008-5472.CAN-05-4414

P. Jaccard, Nouvelles recherches sur la distribution florale, Bulletin de la Société de Vaud des Sciences Naturelles, p.223, 1908.

M. Z. Jahromi and M. Taheri, A proposed method for learning rule weights in fuzzy rule-based classification systems, Fuzzy sets and syst, pp.494-459, 2008.

A. K. Jain and R. C. Dubes, Algorithms for clustering data, 1988.

A. K. Jain, M. N. Murty, and P. J. Flynn, Data clustering: a review, ACM Computing Surveys, vol.31, issue.3, p.31, 1999.
DOI : 10.1145/331499.331504

F. Janicke, A. Prechtl, and C. Thomssen, Randomized Adjuvant Chemotherapy Trial in High-Risk, Lymph Node-Negative Breast Cancer Patients Identified by Urokinase-Type Plasminogen Activator and Plasminogen Activator Inhibitor Type 1, JNCI Journal of the National Cancer Institute, vol.93, issue.12, pp.913-920, 2001.
DOI : 10.1093/jnci/93.12.913

J. M. Jerez, J. I. Peláez, A. Condoretty, and E. Alba, A Neuro-fuzzy Decision Model for Prognosis of Breast cancer relapse, LNAI 3040, pp.638-645, 2004.

E. L. Kaplan, In a retrospective on the seminal paper in "This week's citation classic, Current Contents, vol.24, issue.14, 1983.

E. L. Kaplan and P. Meier, Nonparametric Estimation from Incomplete Observations, Journal of the American Statistical Association, vol.37, issue.282, pp.457-481, 1958.
DOI : 10.1214/aoms/1177731566

J. Khan, J. Wei, and M. Ringner, Classification and Diagnostic Prediction of Cancers Using Gene Expression Profiling and Artificial Neural Networks, Nature Medicine, vol.7, issue.6, pp.673-679, 2001.
DOI : 10.1038/89044

M. U. Khan, J. P. Choi, H. Shin, and M. Kim, Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare, Conf Proc IEEE Eng Med Biol Soc, pp.5148-5151, 2008.

J. Kiefer, Sequential minimax search for a maximum, Proc. 4th Amer Math. Soc, pp.502-506, 1953.

K. Kira and L. Rendell, A Practical Approach to Feature Selection, proced. 9 th Int'l Workshop on Machine Learning, pp.249-256, 1992.
DOI : 10.1016/B978-1-55860-247-2.50037-1

K. Kira and L. Rendell, The Feature Selection Problem: Traditional Methods and a New Algorithm, Proc. AAAI 129-134, 1992.

K. Koh, S. J. Kim, and B. S. , An interior-point method for large-scale ? 1 -regularized logistic regression, J. Mach. Learn. Res, vol.8, pp.1519-1555, 2007.

R. Kohavi and G. H. 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

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological Cybernetics, vol.13, issue.1, pp.59-69, 1982.
DOI : 10.1007/BF00337288

G. E. Konecny, Y. G. Meng, and M. Untch, Association between HER-2/neu and Vascular Endothelial Growth Factor Expression Predicts Clinical Outcome in Primary Breast Cancer Patients, Clinical Cancer Research, vol.10, issue.5, pp.1706-1716, 2004.
DOI : 10.1158/1078-0432.CCR-0951-3

I. Kononenko, Estimating attributes: Analysis and extensions of RELIEF, Proc. European Conf. Mach. Learning ECML, pp.171-182, 1994.
DOI : 10.1007/3-540-57868-4_57

S. Koscielny, Critical review of microarray-based prognostic tests and trials in breast cancer, Current Opinion in Obstetrics and Gynecology, pp.47-50, 2008.

B. Kuipers, Qualitative Reasoning: Modeling and simulation with Incomplete Knowledge, 1994.

J. Lafferty and L. Wasserman, Challenges in statistical machine learning, pp.307-322, 2006.

W. H. Land and E. A. Verheggen, Multiclass primal support vector machines for breast density classification, Int J Comput Biol Drug Des, vol.2, issue.1, pp.21-57, 2009.

C. C. Lee, Fuzzy logic in control systems: fuzzy logic controller. I, IEEE Transactions on Systems, Man, and Cybernetics, vol.20, issue.2, pp.404-435, 1990.
DOI : 10.1109/21.52551

J. K. Lee, D. M. Havaleshko, and H. Cho, A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery, Proceedings of the National Academy of Sciences of the United States of America, pp.13086-13091, 2007.
DOI : 10.1073/pnas.0610292104

M. T. Lee, F. C. Kuo, G. A. Whitemore, and J. Sklar, Importance of replication in microarray gene expression studies: Statistical methods and evidence from repetitive cDNA hybridizations, Proc. Natl. Acad. Sci. USA 97, pp.9834-9839, 2000.
DOI : 10.1073/pnas.97.18.9834

M. Lee and Y. Chi-shih, Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images, Computer methods and programs in biomedicine, pp.269-282, 2010.

S. I. Lee, H. Lee, P. Abbeel, and A. Y. Ng, Efficient ? 1 regularized logistic regression, Proc. 21st AAAI Conf, pp.1-9, 2006.

Y. Lee, Support Vector Machines for Classification: A Statistical Portrait, Methods Mol Biol, vol.620, pp.347-368, 2010.
DOI : 10.1007/978-1-60761-580-4_11

J. Li, Z. Zhang, J. Rosenzweig, Y. Y. Wang, and D. W. Chan, Proteomics and bioinformatics approaches for identification of serum biomarkers to detect breast cancer, Clinical chemistry, issue.8, pp.48-1296, 2002.

T. Li, C. Zhang, and M. Ogihara, A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression, Bioinformatics, vol.20, issue.15, pp.2429-2437, 2004.
DOI : 10.1093/bioinformatics/bth267

Y. Li and Z. Wu, Fuzzy feature selection based on min???max learning rule and extension matrix, Pattern Recognition, vol.41, issue.1, pp.217-226, 2008.
DOI : 10.1016/j.patcog.2007.06.007

Y. Li and B. Lu, Feature selection based on loss-margin of nearest neighbor classification, Pattern Recognition, vol.42, issue.9, 1914.
DOI : 10.1016/j.patcog.2008.10.011

H. Liu, F. Hussian, C. L. Tan, and M. Dash, Discretization: an enabling technique, Data Mining and Knowledge Discovery, vol.6, issue.4, pp.393-423, 2002.
DOI : 10.1023/A:1016304305535

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

H. X. Liu, R. S. Zhang, and F. Luan, Diagnosing Breast Cancer Based on Support Vector Machines, Journal of Chemical Information and Computer Sciences, vol.43, issue.3, pp.900-907, 2003.
DOI : 10.1021/ci0256438

P. J. Lucas, Model-based diagnosis in medicine, Artificial Intelligence in Medicine, vol.10, issue.3, pp.201-208, 1997.
DOI : 10.1016/S0933-3657(97)00392-8

X. Ma, J. Z. Wang, and P. D. Ryan, A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen, Cancer Cell, vol.5, issue.6, pp.607-616, 2004.
DOI : 10.1016/j.ccr.2004.05.015

P. S. Maclin, J. Dempsey, J. Brooks, and R. J. , Using neural networks to diagnose cancer, Using neural networks to diagnose cancer, pp.11-19, 1991.
DOI : 10.1007/BF00993877

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the 5 th Berkely Symposium on Mathematical Statistics and Probability, pp.281-297, 1967.

A. Makris, T. J. Powles, and S. E. Ashley, A reduction in the requirements for mastectomy in a randomized trial of neoadjuvant chemoendocrine therapy in primary breast cancer, Annals of Oncology, vol.9, issue.11, pp.1179-1184, 1998.
DOI : 10.1023/A:1008400706949

O. L. Mangasarian, Exact 1-Norm support vector machines via unconstrained convex differentiable minimization, J. Mach. Learn. Res, vol.7, pp.1517-1530, 2006.

Y. Matsumura and D. Tarin, Significance of CD44 gene products for cancer diagnosis and disease evaluation, The Lancet, pp.1053-1058, 1992.

D. Mauri, N. Pavlidis, and J. P. Ioannidis, Neoadjuvant Versus Adjuvant Systemic Treatment in Breast Cancer: A Meta-Analysis, JNCI Journal of the National Cancer Institute, vol.97, issue.3, pp.188-194, 2005.
DOI : 10.1093/jnci/dji021

S. Medasani and J. Kim, An overview of membership function generation techniques for pattern recognition, International Journal of Approximate Reasoning, vol.19, issue.3-4, pp.391-417, 1998.
DOI : 10.1016/S0888-613X(98)10017-8

S. Mian, S. Ugurel, and E. Parkinson, Serum Proteomic Fingerprinting Discriminates Between Clinical Stages and Predicts Disease Progression in Melanoma Patients, Journal of Clinical Oncology, vol.23, issue.22, pp.5088-5093, 2005.
DOI : 10.1200/JCO.2005.03.164

R. S. Michalski and R. E. Stepp, Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.5, issue.4, pp.396-410, 1980.
DOI : 10.1109/TPAMI.1983.4767409

S. Michiels, D. Koscielny, and C. Hill, Prediction of cancer outcome with microarrays, The Lancet, vol.365, issue.9472, pp.488-492, 2005.
DOI : 10.1016/S0140-6736(05)66539-7

S. Mika, G. Ratsch, J. Weston, B. Scholkopf, and K. R. Mullers, Fisher discriminant analysis with kernels, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468), 1999.
DOI : 10.1109/NNSP.1999.788121

L. D. Miller, J. Smeds, and J. George, From The Cover: An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival, Proceedings of the National Academy of Sciences, vol.102, issue.38, pp.13550-13555, 2005.
DOI : 10.1073/pnas.0506230102

P. Mitra, C. A. Murthy, and S. K. Pal, Unsupervised feature selection using feature similarity, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.3, pp.301-312, 2002.
DOI : 10.1109/34.990133

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

T. Mohri and T. Hidehiko, An optimal Weighting Criterion of case indexing for both numeric and symbolic attributes Case-based Reasoning workshop, pp.123-127, 1994.

A. Y. Ng, regularization, and rotational invariance, Twenty-first international conference on Machine learning , ICML '04, pp.78-86, 2004.
DOI : 10.1145/1015330.1015435

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

J. Nocedal and W. S. , Numerical optimization, 1999.
DOI : 10.1007/b98874

J. P. Novak, R. Sladek, and T. J. Hudson, Characterization of Variability in Large-Scale Gene Expression Data: Implications for Study Design, Genomics, vol.79, issue.1, pp.79-104, 2002.
DOI : 10.1006/geno.2001.6675

M. Nykter, T. Aho, M. Ahdesmäki, and P. Ruusuvuori, Simulation of microarray data with realistic characteristics, BMC Bioinformatics, vol.7, issue.1, pp.332-349, 2006.

I. A. Olivotto, C. D. Bajdik, and P. M. Ravdin, Population-Based Validation of the Prognostic Model ADJUVANT! for Early Breast Cancer, Journal of Clinical Oncology, vol.23, issue.12, pp.2716-2725, 2005.
DOI : 10.1200/JCO.2005.06.178

A. B. Olshen and A. N. Jain, Deriving quantitative conclusions from microarray expression data, Bioinformatics, vol.18, issue.7, pp.961-970, 2002.
DOI : 10.1093/bioinformatics/18.7.961

S. Paik, S. Shak, and G. Tang, A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast Cancer, New England Journal of Medicine, vol.351, issue.27, pp.351-2817, 2004.
DOI : 10.1056/NEJMoa041588

R. M. Parry, W. Jones, and T. H. Stokes, k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction, The Pharmacogenomics Journal, vol.23, issue.4, pp.961-970, 2002.
DOI : 10.1038/sj.bjc.6602876

E. A. Perez, V. J. Suman, and N. E. Davidson, Testing by Local, Central, and Reference Laboratories in Specimens From the North Central Cancer Treatment Group N9831 Intergroup Adjuvant Trial, Journal of Clinical Oncology, vol.24, issue.19, pp.3032-3038, 2006.
DOI : 10.1200/JCO.2005.03.4744

C. M. Perou, T. Sorlie, and M. B. Eisen, Molecular portraits of human breast tumours, Nature, vol.52, pp.406-747, 2000.

N. Piera and J. Aguilar-martin, Controlling selectivity in non-standard pattern recognition algorithms, IEEE Trans. on Systems, Man and Cybernetics, vol.21, issue.1, 1991.

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

T. Raemaekers, K. Ribbeck, and J. Beaudouin, NuSAP, a novel microtubule-associated protein involved in mitotic spindle organization, The Journal of Cell Biology, vol.103, issue.6, pp.1017-1029, 2003.
DOI : 10.1083/jcb.116.6.1303

S. Ramaswamy, P. Tamayo, and R. Rifkin, Multiclass cancer diagnosis using tumor gene expression signatures, Proc. Nat'l Acad. Sc. USA, pp.98-15149, 2001.
DOI : 10.1073/pnas.211566398

K. A. Rasmani and Q. Shen, Modifying weighted fuzzy subsethood-based rule models with fuzzy quantifiers, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), pp.1679-1684, 2004.
DOI : 10.1109/FUZZY.2004.1375433

J. F. Reid, L. Lusa, and L. De-cecco, Limits of Predictive Models Using Microarray Data for Breast Cancer Clinical Treatment Outcome, JNCI J Natl Cancer Inst, issue.12, pp.97-927, 2005.

J. S. Reis-filho, C. Westbury, and J. Pierga, The impact of expression profiling on prognostic and predictive testing in breast cancer, Journal of Clinical Pathology, vol.59, issue.3, pp.225-231, 2006.
DOI : 10.1136/jcp.2005.028324

J. D. Rennie and N. Srebro, Fast maximum margin matrix factorization for collaborative prediction, Proceedings of the 22nd international conference on Machine learning , ICML '05, pp.713-719, 2005.
DOI : 10.1145/1102351.1102441

H. Ressom, R. Reynolds, and R. S. Varghese, Increasing the efficiency of fuzzy logic-based gene expression data analysis, Physiological Genomics, vol.13, issue.2, pp.107-117, 2003.
DOI : 10.1152/physiolgenomics.00097.2002

R. M. Ripley, A. L. Harris, and L. Tarassenko, Non-linear survival analysis using neural networks, Statistics in Medicine, vol.41, issue.5, pp.825-842, 2004.
DOI : 10.1002/sim.1655

M. Robnik-?ikonja and I. Kononenko, Theoretical and empirical analysis of ReliefF and RReliefF, Machine Learning, pp.23-69, 2003.

D. M. Rodvold, D. G. Mcleod, J. M. Brandt, P. B. Snow, and G. P. Murphy, introduction to artificial neural networks for physicians: taking the lid of the black box, Prostate, pp.46-85, 2001.

F. Rosenblatt, Principle of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, Spartan Books, pp.218-219, 1962.

S. Rosset, J. Zhu, and T. Hastie, Boosting as a regularized path to a maximum margin classifier, J. Mach. Learn. Res, vol.5, pp.941-973, 2004.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, Learning representations by back-propagating errors, Nature, vol.85, issue.6088, pp.533-536, 1986.
DOI : 10.1038/323533a0

Y. Saeys, I. Inza, and P. Larrañaga, A review of feature selection techniques in bioinformatics, Bioinformatics, vol.23, issue.19, pp.2507-2517, 2007.
DOI : 10.1093/bioinformatics/btm344

J. A. Sanz, A. Fernández, H. Bustince, and F. Herrera, Improving the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets and genetic amplitude tuning, Information Sciences, vol.180, issue.19, pp.3674-3685, 2010.
DOI : 10.1016/j.ins.2010.06.018

M. Schmidt, G. Fung, and R. Rosales, Fast optimization methods for L1 regularization:a comparative study and two new approaches, Proc. 18th Euro. Conf. Mach. Learn, pp.286-297, 2007.

F. Schnieders, T. Dörk, and J. Arnemann, Testis-specific protein, Y-encoded (TSPY) expression in testicular tissues, Human Molecular Genetics, vol.5, issue.11, pp.1801-1807, 1996.
DOI : 10.1093/hmg/5.11.1801

Q. Sheng, Y. Moreau, D. Moor, and B. , Biclustering microarray data by Gibbs sampling, Bioinformatics, vol.19, issue.Suppl 2, pp.196-205, 2003.
DOI : 10.1093/bioinformatics/btg1078

M. Shipp, K. Ross, and P. Tamayo, Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning, Nature Medicine, vol.8, issue.1, pp.68-74, 2002.
DOI : 10.1038/nm0102-68

R. J. Simes, Treatment selection for cancer patients: Application of statistical decision theory to the treatment of advanced ovarian cancer, Journal of Chronic Diseases, vol.38, issue.2, pp.171-186, 1985.
DOI : 10.1016/0021-9681(85)90090-6

S. E. Singletary, C. Allred, and P. Ashley, Revision of the American Joint Committee on Cancer Staging System for Breast Cancer, Journal of Clinical Oncology, vol.20, issue.17, pp.3628-3636, 2002.
DOI : 10.1200/JCO.2002.02.026

R. E. Smith, A review of selective estrogen receptor modulators and national surgical adjuvant breast and bowel project clinical trials, Seminars in Oncology, vol.30, pp.4-13, 2003.
DOI : 10.1053/j.seminoncol.2003.08.002

C. Sotiriou, S. Neo, and L. M. Mcshane, Breast cancer classification and prognosis based on gene expression profiles from a population-based study, Proceedings of the National Academy of Sciences, vol.100, issue.18, pp.100-10393, 2003.
DOI : 10.1073/pnas.1732912100

A. J. Stephenson, A. Smith, and M. W. Kattan, Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy, Cancer, vol.23, issue.2, pp.290-298, 2005.
DOI : 10.1002/cncr.21157

M. E. Straver, A. M. Glas, and J. Hannemann, The 70-gene signature as a response predictor for neoadjuvant chemotherapy in breast cancer, Breast Cancer Research and Treatment, vol.19, issue.3, pp.551-558, 2009.
DOI : 10.1007/s10549-009-0333-1

URL : https://hal.archives-ouvertes.fr/hal-00535339

M. Sugeno, An introductory survey of fuzzy control, Information Sciences, vol.36, issue.1-2, pp.59-83, 1997.
DOI : 10.1016/0020-0255(85)90026-X

Y. Sun, S. Todorovic, J. Li, and D. Wu, Unifying Error-Correcting and Output-Code AdaBoost through the Margin Concepts, proceedings of 22 nd Int'l Conf. on Machine Learning, pp.872-879, 2005.
DOI : 10.1145/1102351.1102461

Y. Sun, S. Goodison, J. Li, L. Liu, and W. Farmerie, Improved breast cancer prognosis through the combination of clinical and genetic markers, Bioinformatics, vol.23, issue.1, pp.30-37, 2007.
DOI : 10.1093/bioinformatics/btl543

Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.6, pp.1035-1051, 2007.
DOI : 10.1109/TPAMI.2007.1093

J. A. Swets, Signal detection theory and ROC analysis in psychology and diagnostics: collected papers, Scientific psychology series, 1996.

P. Tamayo, D. Slonim, and J. Mesirov, Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation, Proceedings of the National Academy of Sciences, vol.96, issue.6, pp.2907-2912, 1999.
DOI : 10.1073/pnas.96.6.2907

R. Tibshirani, Regression shrinkage and selection via the LASSO, J. R. Stat. Soc. Ser. B, vol.58, pp.267-288, 1996.

Y. Tu, G. Stolovitzky, and U. Klein, Quantitative noise analysis for gene expression microarray experiments, Proceedings of the National Academy of Sciences, vol.99, issue.22, pp.99-14031, 2002.
DOI : 10.1073/pnas.222164199

M. J. Van-de-vijver, Y. D. He, and L. J. Van-'t-veer, A Gene-Expression Signature as a Predictor of Survival in Breast Cancer, New England Journal of Medicine, vol.347, issue.25, 1999.
DOI : 10.1056/NEJMoa021967

L. J. Van-'t-veer, H. Dai, and M. J. Van-de-vijver, Gene expression profiling predicts clinical outcome of breast cancer, Nature, vol.415, issue.6871, pp.530-536, 2002.
DOI : 10.1038/415530a

V. N. Vapnik, Statistical Learning Theory, 1998.

A. Vellido and P. J. Lisboa, Neural Networks and Other Machine Learning Methods in Cancer Research, LNCS, vol.4507, pp.964-971, 2007.
DOI : 10.1007/978-3-540-73007-1_116

C. L. Vogel, M. A. Cobleigh, and D. Tripathy, -Overexpressing Metastatic Breast Cancer, Journal of Clinical Oncology, vol.20, issue.3, pp.719-726, 2002.
DOI : 10.1200/JCO.2002.20.3.719

URL : https://hal.archives-ouvertes.fr/hal-00159653

J. Wang, T. H. Bø, and I. Jonassen, Tumor classification and marker gene prediction by feature selection and fuzzy c-means clustering using microarray data, BMC bioinformatics, vol.4, issue.60, 2003.

W. Wang and Y. Zhang, On fuzzy cluster validity indices, Fuzz. Set and sys, pp.2095-2117, 2007.

X. Wang, Y. Wang, and L. Wang, Improving fuzzy c-means clustering based on feature-weight learning, Pattern Recognition Letters, vol.25, issue.10, pp.1123-1132, 2004.
DOI : 10.1016/j.patrec.2004.03.008

Y. Wang, J. G. Klijn, and Y. Zhang, Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer, The Lancet, vol.365, issue.9460, pp.671-679, 2005.
DOI : 10.1016/S0140-6736(05)70933-8

Y. Wang, I. V. Tetko, and M. A. Hall, Gene selection from microarray data for cancer classification???a machine learning approach, Computational Biology and Chemistry, vol.29, issue.1, pp.37-46, 2005.
DOI : 10.1016/j.compbiolchem.2004.11.001

H. Wei and S. A. Billings, Feature Subset Selection and Ranking for Data Dimensionality Reduction, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.1, pp.162-166, 2007.
DOI : 10.1109/TPAMI.2007.250607

J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, and V. Vapnik, Feature Selection for SVMs, Advances in Neural Information Processing Systems, pp.668-674, 2001.

D. Wettschereck and D. W. Aha, Weighting features, Case-Based Reasoning, Research and Development Lecture Notes in Computer Science, pp.347-358, 1010.

D. Wettschereck, D. W. Aha, and T. Mohri, A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms, Artificial Intelligence Review, vol.11, issue.1, pp.273-314, 1997.
DOI : 10.1007/978-94-017-2053-3_11

S. M. Wiseman, N. Makretsov, and T. O. Nielsen, Coexpression of the type 1 growth factor receptor family members HER-1, HER-2, and HER-3 has a synergistic negative prognostic effect on breast carcinoma survival, Cancer, vol.30, issue.9, pp.1770-1777, 2005.
DOI : 10.1002/cncr.20970

W. H. Wolberg, W. N. Street, and M. O. , Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates, Cancer Letters, vol.77, issue.2-3, pp.163-171, 1994.
DOI : 10.1016/0304-3835(94)90099-X

S. L. Wong, C. Chao, and M. J. Edwards, Frequency of sentinel lymph node metastases in patients with favorable breast cancer histologic subtypes, The American Journal of Surgery, vol.184, issue.6, pp.492-498, 2002.
DOI : 10.1016/S0002-9610(02)01057-7

M. Wygralak, An axiomatic approach to scalar cardinalities of fuzzy sets, Fuzzy Sets and Systems, pp.175-179, 2000.

E. Xing, M. Jordan, and R. Karp, Feature selection for high dimensional genomic microarray data, Proc. 15 th Int'l Conf. Machine Learning, pp.601-608, 2001.

R. Xu and D. I. Wunch, Survey of Clustering Algorithms, IEEE Transactions on Neural Networks, vol.16, issue.3, pp.645-678, 2005.
DOI : 10.1109/TNN.2005.845141

R. Yang, C. K. Ng, and S. M. Wasserman, Identification of a novel family of cellsurface proteins expressed in human vascular endothelium, J. Biol. Chem, vol.227, pp.46364-46373, 2002.

J. Yao, M. Dash, S. T. Tan, and H. Liu, Entropy-based fuzzy clustering and fuzzy modeling, Fuzz. Set and Sys, pp.381-388, 2000.

L. A. Zadeh, Fuzzy sets and systems theory, In: Fox J editor, Polytechnic press, pp.29-37, 1965.

L. A. Zadeh, Biological applications of the theory of fuzzy sets and systems, proced. Of an international symposium on Biocybernetics of the Central Nervous System, pp.199-206, 1969.

B. Zheng, X. Wang, D. Lederman, J. Tan, and D. Gur, Computer-aided detection; the effect of training databases on detection of subtle breast masses, Pharmacogenomics J, vol.10, issue.4, pp.292-309, 2010.

X. Zhou, M. Tan, and V. Stone-hawthorne, Activation of the Akt/Mammalian Target of Rapamycin/4E-BP1 Pathway by ErbB2 Overexpression Predicts Tumor Progression in Breast Cancers, Clinical Cancer Research, vol.10, issue.20, pp.6779-6788, 2004.
DOI : 10.1158/1078-0432.CCR-04-0112

J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, 1-norm support vector machines, Proc. 16th Adv. Neu. Info. Proc. Sys, pp.49-56, 2003.

P. Zindy, Y. Bergé, and B. C. Allal, Formation of the eIF4F translation initiation complex determines sensitivity to anti-cancer drugs targeting the EGFR and HER2 receptors, Cancer Research, pp.10-1158, 2011.

R. Zwick, E. Carlstein, and D. V. Budescu, Measures of similarity among fuzzy concepts: A comparative analysis, International Journal of Approximate Reasoning, vol.1, issue.2, pp.221-242, 1987.
DOI : 10.1016/0888-613X(87)90015-6