Evaluation of SMOTE for high-dimensional class-imbalanced microarray data

Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012

Volumn 2, Issue , 2012, Pages 89-94

  Blagus, Rok ^a   Lusa, Lara ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

class imbalance; high dimensional; SMOTE

Indexed keywords

CLASS-IMBALANCE; EUCLIDEAN DISTANCE; GENE EXPRESSION MICROARRAY; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL; K-NN CLASSIFICATIONS; K-NN CLASSIFIER; MICROARRAY DATA; OVER SAMPLING; RANDOM UNDER SAMPLINGS; SMOTE; SYNTHETIC MINORITY OVER-SAMPLING TECHNIQUES; VARIABLE SELECTION;

GENE EXPRESSION;

LEARNING SYSTEMS;

EID: 84873571735     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2012.183     Document Type: Conference Paper

References (14)

1. S. Ramaswamy, K. N. Ross, E. S. Lander, and T. R. Golub, "A molecular signature of metastasis in primary solid tumors," Nature Genetics, vol. 33, no. 1, pp. 4954, January 2003. (Pubitemid 36068681)
2. M. A. Shipp, K. N. Ross, P. Tamayo, A. P. Weng, J. L. Kutok, R. C. Aguiar, M. Gaasenbeek, M. Angelo, M. Reich, G. S. Pinkus, T. S. Ray, M. A. Koval, K. W. Last, A. Norton, T. A. Lister, J. Mesirov, D. S. Neuberg, E. S. Lander, and J. C. Aster, "Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning." Nature Medicine, vol. 8, no. 1, p. 68, 2002.
3. N. Iizuka, M. Oka, H. Yamada-Okabe, M. Nishida, Y. Maeda, N. Mori, T. Takao, T. Tamesa, A. Tangoku, H. Tabuchi, K. Hamada, H. Nakayama, H. Ishitsuka, T. Miyamoto, A. Hirabayashi, S. Uchimura, and Y. Hamamoto, "Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection," The Lancet, vol. 361, no. 9361, pp. 923 - 929, 2003. (Pubitemid 36332108)
4. H. He and E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 12631284, 2009.
5. R. Blagus and L. Lusa, "Class prediction for high-dimensional classimbalanced data," BMC Bioinformatics, vol. 11, no. 1, p. 523, 2010.
6. 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. 341-378, 2002.
7. J. V. Hulse, T. M. Khoshgoftaar, and A. Napolitano, "Experimental perspectives on learning from imbalanced data," in Proceedings of the Twenty-Fourth International Conference (ICML), 2007, pp. 935-942.
8. B. Wallace, K. Small, C. Brodley, and T. Trikalinos, "Class imbalance, redux," in Data Mining (ICDM), 2011 IEEE 11th International Conference on, dec. 2011, pp. 754 763.
9. T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 8, pp. 861 - 874, 2006.
10. C. Sotiriou, S.-Y. Neo, L. M. McShane, E. L. Korn, P. M. Long, A. Jazaeri, P. Martiat, S. B. Fox, A. L. Harris, and E. T. Liu, "Breast cancer classification and prognosis based on gene expression profiles from a population-based study," Proceedings of the National Academy of Science USA, vol. 100, no. 18, pp. 10 39310 398, September 2003. (Pubitemid 37071889)
11. J. Pittman, E. Huang, H. Dressman, C. Horng, S. Cheng, M. Tsou, C. Chen, A. Bild, E. Iversen, A. Huang, J. Nevins, and M. West, "Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes." Proceedings of National Academy of Science USA, vol. 101, no. 22, pp. 8431-6, 2004. (Pubitemid 38736592)
12. L. D. Miller, J. Smeds, J. George, V. B. Vega, L. Vergara, A. Ploner, Y. Pawitan, P. Hall, S. Klaar, E. T. Liu, and et al., "An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival." Proceedings of the National Academy of Sciences of the United States of America, vol. 102, no. 38, pp. 13 55013 555, 2005. (Pubitemid 41420886)
13. J. Demsar, "Statistical comparisons of classifiers over multiple data sets," Journal of Machine Learning Research, vol. 7, pp. 1-30, Dec. 2006.
14. S. Dudoit, J. Fridlyand, and T. P. Speed, "Comparison of discrimination methods for the classification of tumors using gene expression data," Journal of the American Statistical Association, vol. 97, no. 457, pp. 7787, 2002.