[1. Alcalá-Fdez, J., L. Sánchez, S. García, M. J. del Jesus, S. Ventura, J. M. Garrell, J. Otero, C. Romero, J. Bacardit, V. M. Rivas, J. C. Fernández, F. Herrera. KEEL: A Software Tool to Assess Evolutionary Algorithms to Data Mining Problems. - Soft Computing, Vol. 13, 2009, No 3, pp. 307-318.10.1007/s00500-008-0323-y]Search in Google Scholar
[2. Alcalá-Fdez, J., A. Fernandez, J. Luengo, J. Derrac, S. García, L. Sánchez, F. Herrera. 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, 2011, No 2-3, pp. 255-287.]Search in Google Scholar
[3. Barandela, R., J. S. Sanchez, V. Garcia, E. Rangel. Strategies for Learning in Class Imbalance Problems. - Pattern Recogn., Vol. 36, 2003, No 3, pp. 849-851.10.1016/S0031-3203(02)00257-1]Search in Google Scholar
[4. Krawczyk, B. Learning from Imbalanced Data: Open Challenges and Future Directions. - Progress in Artificial Intelligence, Vol. 5, November 2016, No 4, pp. 221-232.10.1007/s13748-016-0094-0]Search in Google Scholar
[5. Chawla, N. V., K.,W. Bowyer, L.,O. Hall, W. P. Kegelmeyer. SMOTE: Synthetic Minority Over-Sampling Technique. - J. Artificial Intelligence Research, Vol. 16, 2002, pp. 321-357.10.1613/jair.953]Search in Google Scholar
[6. Chawla, N. V., N. Japkowicz, A. Kolcz. Editorial: Special Issue on Learning from Imbalanced Data Sets. - ACM SIGKDD Explorations Newsletter, Vol. 6, 2004, No 1, pp. 1-6.10.1145/1007730.1007733]Search in Google Scholar
[7. Ramentol, E., S. Vluymans, N. Verbiest, Y. Caballero, R. Bello, C. Cornelis, F. Herrera. IFROWANN: Imbalanced Fuzzy-Rough Ordered Weighted Average Nearest Neighbor Classification. - IEEE Transactions on Fuzzy Systems, Vol. 23, October 2015, No 5, pp. 1622-1637.10.1109/TFUZZ.2014.2371472]Search in Google Scholar
[8. Frank, A., A. Asuncion. UCImachine learning repository. 2010. http://archive.ics.uci.edu/ml]Search in Google Scholar
[9. Galar, M., A. Fernando, E. Barrenechea, H. Business, F. Herrera. A Review on Ensembles for the Class Imbalance Problem. - IEEE Transactions on Systems, Man, and Cybernetics. Part C: Applications and Review, Vol. 42, 2012.10.1109/TSMCC.2011.2161285]Search in Google Scholar
[10. Garcia-Pedrajas, N., J. Perez-Rodriguez, M. Garcia-Pedrajas, D. Ortiz- Boyer, C. Fyfe. Class Imbalance Methods for Translation Initiation Site Recognition in DNA Sequences. - Knowl. Based Syst., Vol. 25, 2012, No 1, pp. 22-34.10.1016/j.knosys.2011.05.002]Search in Google Scholar
[11. Guo, H., H. Viktor. Learning from Imbalanced Data Sets with Boosting and Data Generation: The Databoost-im Approach. - SIGKDD Explorations, Vol. 6, 2004, pp. 30-39.10.1145/1007730.1007736]Search in Google Scholar
[12. Lee, J., D.-W. Kim. Mutual Information-Based Multi-Label Feature Selection Using Interaction Information. - Expert Systems with Applications, Vol. 42, March 2015, No 4, pp. 2013-2025.10.1016/j.eswa.2014.09.063]Search in Google Scholar
[13. Jain, A., B. Chandrasekharan. Dimensionality and Sample Size Considerations in Pattern Recognition Practice. - In: P. Krishnaiah, L. Kanal, Eds. Handbook of Statistics. Vol. 2. North Holland, 1982, pp. 835-855.10.1016/S0169-7161(82)02042-2]Search in Google Scholar
[14. Jo, T., N. Japkowicz. Class Imbalances Versus Small Disjuncts. - ACM SIGKDD, Vol. 6, 2004, No 1, pp. 40-4910.1145/1007730.1007737]Search in Google Scholar
[15. Sáez, J. A., B. Krawczyk, M. Woźniak. Analyzing the Oversampling of Different Classes and Types of Examples in Multi-Class Imbalanced Datasets. - Pattern Recogn., Vol. 57, 2016, pp. 164-178.10.1016/j.patcog.2016.03.012]Search in Google Scholar
[16. Peng, L., et al. Imbalanced Traffic Identification Using an Imbalanced Data Gravitation-Based Classification Model. - Computer Communications, 2016, pp. 347-373.]Search in Google Scholar
[17. Moreno-Torres, J. G., F. Herrera. A Preliminary Study on Overlapping and Data Fracture in Imbalanced Domains by Means of Genetic Programming-Based Feature Extraction. - In: 10th International Conference on Intelligent Systems Design and Applications (ISDA’2010), 2010, pp. 501-506,10.1109/ISDA.2010.5687214]Search in Google Scholar
[18. Moreno-Torres, J. G., T. Raeder, R. Alaíz-Rodríguez, N. V. Chawla, F. Herrera. A Unifying View on Dataset Shift in Classification. - Pattern Recogn., Vol. 45, 2012, No 1, pp. 521-530.10.1016/j.patcog.2011.06.019]Search in Google Scholar
[19. Prati, R. C., G. E. A. P. A. Batista, M. C. Monard. Class Imbalances Versus Class Overlapping: An Analysis ofa Learning System Behavior. - In: R. Monroy, G. Arroyo- Figueroa, L. E. Sucar, H. Sossa, Eds. MICAI 2004. LNCS (LNAI). Vol. 2972. Heidelberg, Springer, 2004, pp. 312-321.]Search in Google Scholar
[20. Yin, Q.-Y., J.-S. Zhang, C.-X. Zhang, N.-N. Ji. A Novel Selective Ensemble Algorithm for Imbalanced Data Classification Based on Exploratory Undersampling. - In: Hindawi Publishing Corporation Mathematical Problems in Engineering. Vol. 2014. Article ID 358942, 14 p.10.1155/2014/358942]Search in Google Scholar
[21. Satuluri, N., M. R. Kuppa. A Novel Class Imbalance Learning Using Intelligent Under- Sampling. - International Journal of Database Theory and Application, Vol. 5, 2012, pp. 25-35.]Search in Google Scholar
[22. Seetha, H., R. Saravanan, M. N. Murty. Pattern Synthesis Using Multiple Kernel Learning for Efficient SVM Classification. - Cybernetics and Information Technologies, Vol. 12, 2012, No 4, pp. 77-94.10.2478/cait-2012-0032]Search in Google Scholar
[23. Sun, Y., M. S. Kamel, A. K. C. Wong, Y. Wang. Cost-Sensitive Boosting for Classification of Imbalanced Data. - Pattern Recogn., Vol. 40, 2007, pp. 3358-3378.10.1016/j.patcog.2007.04.009]Search in Google Scholar
[24. Tahira, M. A., J. Kittlera, F. Yan. Inverse Random under Sampling for Class Imbalance Problem and its Application to Multi-Label Classification. - Pattern Recogn., Vol. 45, 2012, No 10, pp. 3738-3750.10.1016/j.patcog.2012.03.014]Search in Google Scholar
[25. López, V., A. Fernández, F. Herrera. On the Importance of the Validation Technique for Classification with Imbalanced Datasets: Addressing Covariate Shift when Data is Skewed. - Information Sciences, Vol. 257, February 2014, pp. 1-13.10.1016/j.ins.2013.09.038]Search in Google Scholar
[26. Viswanath, P., M. N. Murty, S. Bhatnagar. Partition Based Pattern Synthesis Technique with Efficient Algorithms for Nearest Neighbor Classification. - Pattern Recognition Letters, Vol. 27, 2006, pp. 1714-1724.10.1016/j.patrec.2006.04.015]Search in Google Scholar
[27. Wang, S., X. Yao. Multiclass Imbalance Problems: Analysis and Potential Solutions. - IEEE Trans. Syst., Man, Cybern. B, Vol. 42, 2012, No 4, pp. 1119-1130.10.1109/TSMCB.2012.218728022438514]Search in Google Scholar
[28. Li, Y., X. Zhang. Improving k-Nearest Neighbour with Exemplar Generalization for Imbalanced Classification. Advances in Knowledge Discovery and Data Mining. - In: Lecture Notes in Computer Science. Vol. 6635. 2011, pp. 321-332.]Search in Google Scholar
[29. Zhang, J., I. Mani. KNN Approach to Unbalanced Data Distributions: A Case Study Involving Information Extraction. - In: Proc. of International Conf. Machine Learning (ICML’2003), Workshop Learning from Imbalanced Data Sets, 2003.]Search in Google Scholar
[30. López, V., A. Fernández, S. García, V. Palade, F. Herrera. An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics. - In: Information Sciences. Vol. 250. Online Publication Date: 1 November 2013, pp. 113-141.10.1016/j.ins.2013.07.007]Search in Google Scholar
[31. Borsos, Z., C. Lemnaru, R. Potolea. Dealing with Overlap and Imbalance: A New Metric and Approach. - Pattern Analysis and Applications. Online Publication Date: 27 September 2016.10.1007/s10044-016-0583-6]Search in Google Scholar
[32. Zhang, Z., B. Krawczyk, S. Garcìa, A. Rosales-Pérez, F. Herrera. Empowering One-vs-One Decomposition with Ensemble Learning for Multi-Class Imbalanced Data. - Knowledge-Based Systems, Vol. 106, 2016, pp. 251-263.10.1016/j.knosys.2016.05.048]Search in Google Scholar
[33. Zhou, Z.-H., X.-Y. Liu. On Multi-Class Cost-Sensitive Learning. - Comput. Intell., Vol. 26, 2010, No 3, pp. 232-257.10.1111/j.1467-8640.2010.00358.x]Search in Google Scholar