Class imbalance, redux

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 754-763

  Wallace, Byron C ^a,b   Small, Kevin ^a   Brodley, Carla E ^b   Trikalinos, Thomas A ^a  

^a TUFTS MEDICAL CENTER   (United States)

^b Tufts University   (United States)

Author keywords

Class imbalance; Classification

Indexed keywords

BOOTSTRAP TRAINING; CLASS IMBALANCE; DATA SETS; ENSEMBLE OF CLASSIFIERS; IMBALANCED DATA; PRACTICAL GUIDANCE; PRACTICAL IMPORTANCE; REAL DATA SETS; REAL-WORLD LEARNING; TRAINING DATA;

CLASSIFICATION (OF INFORMATION); DATA MINING;

TEACHING;

EID: 84857180411     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.33     Document Type: Conference Paper

References (21)

1. R. Akbani, S. Kwek, and N. Japkowicz. Applying support vector machines to imbalanced datasets. In ECML, pages 39-50, 2004.
2. L. Breiman. Bagging predictors. Machine Learning, 24(2):123-140, 1996.
3. C.-C. Chang and C. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2(3):27:1-27:27, 2011
4. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357, 2002.
5. G. Cohen, M. Hilario, H. Sax, S. Hugonnet, and A. Geissbuhler. Learning from imbalanced data in surveillance of nosocomial infection. Artificial Intelligence in Medicine, 37(1):7-18, 2006.
6. C. Drummond and R. C. Holte. C4.5, class imbalance, and cost sensitivity: Why under-sampling beats over-sampling. In Proceedings of the ICML Workshop on Learning from Imbalanced Datasets II, 2003.
7. B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. CRC Press, 1993.
8. X. Guo, Y. Yin, C. Dong, G. Yang, and G. Zhou. On the class imbalance problem. In ICNC, pages 192-201, 2008.
9. H. He. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263-1284, 2009.
10. S. Hido and H. Kashima. Roughly balanced bagging for imbalanced data. In SDM, pages 143-152, 2008.
11. J. V. Hulse, T. M. Khoshgoftaar, and A. Napolitano. Experimental perspectives on learning from imbalanced data. In ICML, pages 935-942, 2007.
12. N. Japkowicz and S. Stephen. The class imbalance problem: A systematic study. Intelligent Data Analysis, 6(5):429-449, 2002.
13. P. Kang and S. Cho. EUS SVMs: Ensemble of under-sampled svms for data imbalance problems. In ICONIP, pages 837- 846, 2006.
14. M. Kubat and S. Matwin. Addressing the curse of imbalanced training sets: One-sided selection. In ICML, pages 179-186, 1997.
15. G. R. Lanckriet, L. E. Ghaoui, C. Bhattacharyya, and M. I. Jordan. A robust minimax approach to classification. Journal of Machine Learning Research, 3:555-582, 2002.
16. W. Liu and S. Chawla. A quadratic mean based supervised learning model for managing data skewness. In SDM, 2011.
17. X.-Y. Liu, J. Wu, and Z.-H. Zhou. Exploratory undersampling for class-imbalance learning. In ICDM, pages 965- 969, 2006.
18. J. Reitsma, A. Glas, A. Rutjes, R. Scholten, P. Bossuyt, and A. Zwinderman. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. Journal of Clinical Epidemiology, 58(10):982-990, 2005.
19. D. Tao, X. Tang, X. Li, and X. Wu. Asymmetric bassing and random subspace for support vector machines-based relevance feedback in information retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(7):1088-1099, 2006.
20. S. Wu, K. Lin, C. Chen, and M. Chen. Asymmetric support vector machines: Low false-positive learning under the user tolerance. In KDD, pages 749-757. ACM, 2008.
21. W. Yang and X. Wu. 10 challenging problems in data mining research. International Journal of Information Technology Decision Making, 5(4):597-604, 2006.