?. Alcala, J. Fdez, A. Fernandez, J. Luengo, J. Derrac et al., KEEL Data?Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework, Journal of Multiple?Valued Logic and Soft Computing, vol.17, pp.2-3, 2011.

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

K. Borowska and J. Stepaniuk, Imbalanced Data Classification: A Novel Re-sampling Approach Combining Versatile Improved SMOTE and Rough Sets, Lecture Notes in Computer Science, vol.6, issue.1???3, pp.31-42, 2016.
DOI : 10.1016/j.patcog.2007.04.009
URL : https://hal.archives-ouvertes.fr/hal-01637478

K. Borowska and M. Topczewska, New Data Level Approach for Imbalanced Data Classification Improvement, Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, pp.283-294, 2016.
DOI : 10.1007/978-3-319-26227-7_27

C. Bunkhumpornpat, K. Sinapiromsaran, and C. Lursinsap, Safe-Level?SMOTE: Safe? Level?Synthetic Minority Over?Sampling TEchnique for Handling the Class Imbalanced Problem, Advances in Knowledge Discovery and Data Mining, pp.475-482, 2009.

N. V. Chawla, K. W. Bowyer, L. O. Hall, and K. W. , SMOTE: synthetic minority over-sampling technique, J. Artif. Int. Res, vol.16, issue.1, pp.321-357, 2002.

M. Galar, A. Fernandez, E. Barrenechea, H. Bustince, and F. Herrera, A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.42, issue.4, pp.2012-463
DOI : 10.1109/TSMCC.2011.2161285

V. Garca, R. A. Mollineda, and J. S. Snchez, On the k-NN performance in a challenging scenario of imbalance and overlapping, Pattern Analysis and Applications, vol.27, issue.5, pp.3-4, 2008.
DOI : 10.1002/9781119013563

H. Han, W. Wang, and B. H. Mao, Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, Proceedings of the 2005 international conference on Advances in Intelligent Computing -Volume Part I (ICIC'05), pp.878-887
DOI : 10.1007/11538059_91

K. Krawiec, R. Ss, and D. Vanderpooten, Learning Decision Rules from Similarity Based Rough Approximations, Rough Sets in Knowledge Discovery 2, of the series Studies in Fuzziness and Soft Computing, pp.37-54, 1998.

H. He and E. A. Garcia, Learning from Imbalanced Data, IEEE Trans. on Knowl. and Data Eng, vol.21, issue.9, pp.1263-1284, 2009.

S. Hu, Y. Liang, L. Ma, and Y. He, MSMOTE: Improving Classification Performance When Training Data is Imbalanced, Computer Science and Engineering, WCSE '09. Second International Workshop on, pp.13-17, 2009.
DOI : 10.1109/wcse.2009.756

T. Jo and N. Japkowicz, Class imbalances versus small disjuncts, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.40-49, 2004.
DOI : 10.1145/1007730.1007737

K. Napieraa-la and J. Stefanowski, Types of minority class examples and their influence on learning classifiers from imbalanced data, Journal of Intelligent Information Systems, vol.6, issue.1, pp.563-597, 2016.
DOI : 10.1145/1007730.1007734

K. Napieraa-la, J. Stefanowski, and S. Wilk, Learning from Imbalanced Data in Presence of Noisy and Borderline Examples, Proceedings of the 7th international conference on Rough sets and current trends in computing (RSCTC'10), pp.158-167
DOI : 10.1007/978-3-642-13529-3_18

Z. Pawlak, Rough sets, International Journal of Computer & Information Sciences, vol.8, issue.3, pp.341-356, 1982.
DOI : 10.1007/BF01001956

Z. Pawlak and A. Skowron, Rough sets: Some extensions, Information Sciences, vol.177, issue.1, pp.28-40, 2007.
DOI : 10.1016/j.ins.2006.06.006
URL : http://logic.mimuw.edu.pl/publikacje/ZP-AS-InfSci07-2.pdf

Z. Pawlak and A. Skowron, Rudiments of rough sets, Information Sciences, vol.177, issue.1, pp.3-27, 2007.
DOI : 10.1016/j.ins.2006.06.003

E. Ramentol, Y. Caballero, R. Bello, and F. Herrera, SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory, Knowledge and Information Systems, vol.18, issue.1, pp.2011-245
DOI : 10.1109/TKDE.2006.17

J. A. Saez, J. Luengo, J. Stefanowski, and F. Herrera, SMOTEIPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering, Information Sciences, vol.291, pp.2015-184

J. Stefanowski, Dealing with Data Difficulty Factors While Learning from Imbalanced Data, Challenges in Computational Statistics and Data Mining, pp.333-363, 2016.

J. Stefanowski and S. Wilk, Rough Sets for Handling Imbalanced Data: Combining Filtering and Rule-based Classifiers, Fundam. Inf, vol.72, pp.1-3, 2006.

J. Stepaniuk, Rough?Granular Computing in Knowledge Discovery and Data Mining, 2008.

G. M. Weiss, Mining with rarity, ACM SIGKDD Explorations Newsletter, vol.6, issue.1, pp.7-19, 2004.
DOI : 10.1145/1007730.1007734

D. R. Wilson and T. R. Martinez, Improved heterogeneous distance functions, Journal of Artificial Intelligence Research, vol.6, pp.1-34, 1997.