N. R. Abbasi and H. M. Shaw, Early Diagnosis of Cutaneous Melanoma, JAMA, vol.292, issue.22, pp.2922771-2776, 2004.
DOI : 10.1001/jama.292.22.2771

C. Barata, J. S. Marques, E. Celebi, and M. , Towards an automatic bag-of-features model for the classification of dermoscopy images: The influence of segmentation, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), pp.274-279, 2013.
DOI : 10.1109/ISPA.2013.6703752

C. Barata, M. Ruela, M. Francisco, T. Mendonça, and J. Marques, Two Systems for the Detection of Melanomas in Dermoscopy Images Using Texture and Color Features, IEEE Systems Journal, vol.8, issue.3, pp.965-979, 2014.
DOI : 10.1109/JSYST.2013.2271540

G. E. Batista, A. L. Bazzan, and M. C. Monard, Balancing training data for automated annotation of keywords: a case study, WOB, pp.10-18, 2003.

G. E. Batista, R. C. Prati, and M. C. Monard, A study of the behavior of several methods for balancing machine learning training data, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.20-29, 2004.
DOI : 10.1145/1007730.1007735

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

G. Capdehourat, A. Corez, A. Bazzano, and P. Musé, Pigmented Skin Lesions Classification Using Dermatoscopic Images, Progress in Pattern Recognition , Image Analysis, Computer Vision, and Applications, pp.537-544, 2009.
DOI : 10.1007/978-3-642-10268-4_63

M. E. Celebi and H. A. Kingravi, A methodological approach to the classification of dermoscopy images, Computerized Medical Imaging and Graphics, vol.31, issue.6, pp.31362-373, 2007.
DOI : 10.1016/j.compmedimag.2007.01.003

N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, Smote: synthetic minority oversampling technique, Journal of artificial intelligence research, pp.321-357, 2002.

A. Forsea, D. Marmol, V. De-vries, E. Bailey, E. Geller et al., Melanoma incidence and mortality in Europe: new estimates, persistent disparities, British Journal of Dermatology, vol.46, issue.5, pp.1124-1130, 2012.
DOI : 10.1111/j.1365-2133.2012.11125.x

Z. Guo and D. Zhang, A completed modeling of local binary pattern operator for texture classification, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1657-1663, 2010.

H. He and E. Garcia, Learning from imbalanced data. Knowledge and Data Engineering, IEEE Transactions on, vol.21, issue.9, pp.1263-1284, 2009.

J. Laurikkala, Improving Identification of Difficult Small Classes by Balancing Class Distribution, 2001.
DOI : 10.1007/3-540-48229-6_9

I. Mani and I. Zhang, knn approach to unbalanced data distributions: a case study involving information extraction, Proceedings of Workshop on Learning from Imbalanced Datasets, 2003.

R. C. Prati, G. E. Batista, and M. C. Monard, Data mining with imbalanced class distributions: concepts and methods, IICAI, pp.359-376, 2009.

M. Rastgoo, R. Garcia, O. Morel, and F. Marzani, Automatic differentiation of melanoma from dysplastic nevi, Computerized Medical Imaging and Graphics, vol.43, pp.44-52, 2015.
DOI : 10.1016/j.compmedimag.2015.02.011

URL : https://hal.archives-ouvertes.fr/hal-01457799

M. Rastgoo, O. Morel, F. Marzani, and R. Garcia, Ensemble approach for differentiation of malignant melanoma, The International Conference on Quality Control by Artificial Vision 2015 International Society for Optics and Photonics, pp.953415-953415, 2015.

I. Tomek, Two modifications of cnn, IEEE Trans. Syst. Man Cybern, vol.6, pp.769-772, 1976.

J. Van-de-weijer and C. Schmid, Coloring Local Feature Extraction, Computer Vision?ECCV 2006, pp.334-348, 2006.
DOI : 10.1002/col.10049

URL : https://hal.archives-ouvertes.fr/inria-00548576