KernelADASYN: Kernel based adaptive synthetic data generation for imbalanced learning

2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings

Volumn , Issue , 2015, Pages 664-671

  Tang, Bo ^a   He, Haibo ^a  

^a The University of Rhode Island   (United States)

Author keywords

adaptive over sampling; Imbalanced learning; kernel density estimation; Medical and healthcare data learning; pattern recognition

Indexed keywords

ALGORITHMS; BENCHMARKING; HEALTH CARE; PATTERN RECOGNITION; STATISTICS;

CLASSIFICATION ALGORITHM; CLASSIFICATION PERFORMANCE; IMBALANCED LEARNING; KERNEL DENSITY ESTIMATION; KERNEL DENSITY ESTIMATION METHODS; MEDICAL AND HEALTHCARE DATA LEARNING; OVER SAMPLING; SYNTHETIC DATA GENERATIONS;

CLASSIFICATION (OF INFORMATION);

EID: 84963595704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2015.7256954     Document Type: Conference Paper

References (41)

1. H. He, Y. Bai, E. A. Garcia, and S. Li, "ADASYN: Adaptive synthetic sampling approach for imbalanced learning," in IEEE International Joint Conference on Neural Networks, pp. 1322-1328, 2008.
2. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263-1284, 2009.
3. M. Elter, R. Schulz-Wendtland, and T. Wittenberg, "The prediction of breast cancer biopsy outcomes using two cad approaches that both emphasize an intelligible decision process," Medical Physics, vol. 34, no. 11, pp. 4164-4172, 2007.
4. D. Mease, A. J. Wyner, and A. Buja, "Boosted classification trees and class probability/quantile estimation," The Journal of Machine Learning Research, vol. 8, pp. 409-439, 2007.
5. C. Drummond and R. C. Holte, "C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling," in Workshop on Learning from Imbalanced Datasets II, vol. 11, 2003.
6. X.-Y. Liu, J. Wu, and Z.-H. Zhou, "Exploratory undersampling for class-imbalance learning," IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 2, pp. 539-550, 2009.
7. S. Chen, H. He, and E. A. Garcia, "RAMOBoost: Ranked minority oversampling in boosting," IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1624-1642, 2010.
8. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic minority over-sampling technique," Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
9. N. V. Chawla, A. Lazarevic, L. O. Hall, and K. W. Bowyer, "SMOTEBoost: Improving prediction of the minority class in boosting," in Knowledge Discovery in Databases: PKDD 2003, pp. 107-119, 2003.
10. M. Gao, X. Hong, S. Chen, C. J. Harris, and E. Khalaf, "PDFOS: PDF estimation based over-sampling for imbalanced two-class problems," Neurocomputing, vol. 138, pp. 248-259, 2014.
11. P. Domingos, "Metacost: A general method for making classifiers costsensitive," in Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 155-164, 1999.
12. Z.-H. Zhou and X.-Y. Liu, "Training cost-sensitive neural networks with methods addressing the class imbalance problem," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 63-77, 2006.
13. F. Vilariño, P. Spyridonos, J. Vitrià, and P. Radeva, "Experiments with SVM and stratified sampling with an imbalanced problem: Detection of intestinal contractions," in Pattern Recognition and Image Analysis, pp. 783-791, 2005.
14. P. Kang and S. Cho, "EUS SVMs: Ensemble of under-sampled SVMs for data imbalance problems," in Neural Information Processing, pp. 837-846, 2006.
15. Y. Liu, A. An, and X. Huang, "Boosting prediction accuracy on imbalanced datasets with SVM ensembles," in Advances in Knowledge Discovery and Data Mining, pp. 107-118, 2006.
16. G. Wu and E. Y. Chang, "KBA: Kernel boundary alignment considering imbalanced data distribution," IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 786-795, 2005.
17. X. Hong, S. Chen, and C. J. Harris, "A kernel-based two-class classifier for imbalanced data sets," IEEE Transactions on Neural Networks, vol. 18, no. 1, pp. 28-41, 2007.
18. C. Cortes and V. Vapnik, "Support-vector networks," Machine learning, vol. 20, no. 3, pp. 273-297, 1995.
19. C. M. Bishop, Pattern Recognition and Machine Learning, vol. 4. Springer New York.
20. G. Hinton, S. Osindero, and Y.-W. Teh, "A fast learning algorithm for deep belief nets," Neural computation, vol. 18, no. 7, pp. 1527-1554, 2006.
21. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, pp. 1097-1105, 2012.
22. B. Tang, H. He, Q. Ding, and S. Kay, "A parametric classification rule based on the exponentially embedded family," IEEE Transactions on Neural Networks and Learning Systems, vol. 26, pp. 367-377, Feb 2015.
23. B. Tang, Q. Ding, H. He, and S. Kay, "Hybrid classification with partial models," in IEEE International Joint Conference on Neural Networks, pp. 3726-3731, July 2014.
24. B. Tang and H. He, "ENN: Extended nearest neighbor method for pattern recognition," IEEE Computational Intelligence Magazine, 2015 in press.
25. I. Jolliffe, Principal component analysis. Wiley Online Library, 2002.
26. N. Kambhatla and T. K. Leen, "Dimension reduction by local principal component analysis," Neural Computation, vol. 9, no. 7, pp. 1493-1516, 1997.
27. Y. Xia, H. Tong, W. Li, and L.-X. Zhu, "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 64, no. 3, pp. 363-410, 2002.
28. P. Indyk and R. Motwani, "Approximate nearest neighbors: towards removing the curse of dimensionality," in Proceedings of the thirtieth annual ACM symposium on Theory of computing, pp. 604-613, 1998.
29. J. Xu, G. Yang, Y. Yin, H. Man, and H. He, "Sparse-representation-based classification with structure-preserving dimension reduction," Cognitive Computation, vol. 6, no. 3, pp. 608-621, 2014.
30. J. Xu, Y. Yin, H. Man, and H. He, "Feature selection based on sparse imputation," in IEEE International Joint Conference on Neural Networks, pp. 1-7, 2012.
31. J. Xu, G. Yang, H. Man, and H. He, "L1 graph based on sparse coding for feature selection," in Advances in Neural Networks, pp. 594-601, 2013.
32. H. He and Y. Ma, Imbalanced Learning: Foundations, Algorithms, and Applications. John Wiley & Sons, 2013.
33. T. Fawcett, "ROC graphs: Notes and practical considerations for researchers," Machine learning, vol. 31, pp. 1-38, 2004.
34. D. J. Hand, "Measuring classifier performance: A coherent alternative to the area under the ROC curve," Machine learning, vol. 77, no. 1, pp. 103-123, 2009.
35. J. W. Smith, J. Everhart, W. Dickson, W. Knowler, and R. Johannes, "Using the ADAP learning algorithm to forecast the onset of diabetes mellitus," in Proceedings of the Annual Symposium on Computer Application in Medical Care, p. 261, American Medical Informatics Association, 1988.
36. B. V. Ramana, M. S. P. Babu, and N. Venkateswarlu, "A critical comparative study of liver patients from USA and India: an exploratory analysis," 2012.
37. M. A. Little, P. E. McSharry, S. J. Roberts, D. A. Costello, and I. M. Moroz, "Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection," BioMedical Engineering OnLine, vol. 6, no. 1, p. 23, 2007.
38. E. Berthonnaud, J. Dimnet, P. Roussouly, and H. Labelle, "Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters," Journal of spinal disorders & techniques, vol. 18, no. 1, pp. 40-47, 2005.
39. R. MICHALSKI, "The multi-purpose incremental learning system AQ15 and its testing application to three medical domains," Proceedings. of AAAI-86, Morgan Kaufmann, pp. 1041-1045, 1986.
40. J. Jossinet, "Variability of impedivity in normal and pathological breast tissue," Medical and Biological Engineering and Computing, vol. 34, no. 5, pp. 346-350, 1996.
41. L. A. Kurgan, K. J. Cios, R. Tadeusiewicz, M. Ogiela, and L. S. Goodenday, "Knowledge discovery approach to automated cardiac SPECT diagnosis," Artificial intelligence in medicine, vol. 23, no. 2, pp. 149-169, 2001.