|
1. A. Asuncion, and D. Newman, 'UCI Machine Learning Repository,' 2007
|
|
2. 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, no. 1, pp. 20-29, 2004
|
|
3. J. B?aszczyński, and J. Stefanowski, 'Neighbourhood Sampling in Bagging for Imbalanced Data,' Neurocomputing, vol. 150, pp. 529-542, 2015
|
|
4. A. P. Bradley, 'The Use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms,' Pattern Recognition, vol. 30, no. 7, pp. 1145-1159, 1997
|
|
5. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, 'Ordering-Based Pruning for Improving the Performance of Ensembles of Classifiers in the Framework of Imbalanced Datasets,' Information Sciences, vol. 354, pp. 178-196, 2016
|
|
6. 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,' Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 42, no. 4, pp. 463-484, 2012
|
|
7. S. García, A. Fernández, J. Luengo, and F. Herrera, 'Advanced Nonparametric Tests for Multiple Comparisons in the Design of Experiments in Computational Intelligence and Data Mining: Experimental Analysis of Power,' Information Sciences, vol. 180, no. 10, pp. 2044-2064, 2010
|
|
8. H. Guan, Y. Zhang, M. Xian, H. Cheng, and X. Tang, 'WENN for Individualized Cleaning in Imbalanced Data,' In Pattern Recognition (ICPR), 2016 23rd International Conference on, pp. 456-461, IEEE
|
|
9. Y. Hochberg, 'A Sharper Bonferroni Procedure for Multiple Tests of Significance,' Biometrika, vol. 75, no. 4, pp. 800-802, 1988
|
|
10. R. C. Holte, L. Acker, and B. W. Porter, 'Concept Learning and the Problem of Small Disjuncts,' In IJCAI, pp. 813-818, Citeseer
|
|
11. N. Japkowicz, and S. Stephen, 'The Class Imbalance Problem: A Systematic Study,' Intell Data Anal, vol. 6, no. 5, pp. 429-449, 2002
|
|
12. V. López, A. Fernández, S. García, V. Palade, and F. Herrera, 'An Insight into Classification with Imbalanced Data: Empirical Results and Current Trends on Using Data Intrinsic Characteristics,' Information Sciences, vol. 250, pp. 113-141, 2013
|
|
13. C. X. Ling, Q. Yang, J. Wang, and S. Zhang, 'Decision Trees with Minimal Costs,' In Proceedings of the Twenty-First International Conference on Machine Learning, pp. 69, ACM
|
|
14. K. Napierala, and J. Stefanowski, 'Types of Minority Class Examples and Their Influence on Learning Classifiers from Imbalanced Data,' Journal of Intelligent Information Systems, pp. 1-35, 2015
|
|
15. K. Napiera?a, J. Stefanowski, and S. Wilk, 'Learning from Imbalanced Data in Presence of Noisy and Borderline Examples,' In Rough Sets and Current Trends in Computing, pp. 158-167, Springer
|
|
16. R. C. Prati, G. E. Batista, and M. C. Monard, 'Class Imbalances Versus Class Overlapping: An Analysis of a Learning System Behavior,' MICAI 2004: Advances in Artificial Intelligence, pp. 312-321, Springer, 2004
|
|
17. J. A. Sáez, J. Luengo, J. Stefanowski, and F. Herrera, 'SMOTE–IPF: Addressing the Noisy and Borderline Examples Problem in Imbalanced Classification by a Re-Sampling Method with Filtering,' Information Sciences, vol. 291, no. pp. 184-203, 2015
|
|
18. H. Shohei, K. Hisashi, and T. Yutaka, 'Roughly Balanced Bagging for Imbalanced Data,' Statistical Analysis & Data Mining, vol. 2, no. 2, pp. 412-426, 2009
|
|
19. J. Stefanowski, 'Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data,' Emerging Paradigms in Machine Learning, pp. 277-306, Springer, 2013
|
|
20. Y. Sun, M. S. Kamel, A. K. C. Wong, and Y. Wang, ’‘Cost-Sensitive Boosting for Classification of Imbalanced Data,’‘ Pattern Recognition, vol. 40, no. 12, pp. 3358-3378, 2007
|
|
21. A. Tesfahun and D. L. Bhaskari, 'Intrusion Detection Using Random Forests Classifier with SMOTE and Feature Reduction,' In International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pp. 127-132
|
|
22. H. L. Yu, and J. Ni, 'An Improved Ensemble Learning Method for Classifying High-Dimensional and Imbalanced Biomedicine Data,' IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol. 11, no. 4, pp. 657-666, 2014
|