208
Views
1
CrossRef citations to date
0
Altmetric
The 9th Chinese Data Mining and Applied Statistics Cross-Strait Conference

Grouped Variable Selection Using Area under the ROC with Imbalanced Data

, , , &
Pages 1268-1280 | Received 30 Aug 2012, Accepted 19 Jun 2013, Published online: 14 Apr 2016

References

  • Chawla, N., Bowyer, K., Hall, L., et al. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16:321–357.
  • Chawla, V.N. (2010). Data mining for imbalanced data: An overview. In: Maimon, O., Rokach, L., eds. Data Mining and Knowledge Discovery Handbook. Springer.
  • Drown, D.J., Khoshgoftaar, T.M., Narayanan, R. (2007). Using evolutionary sampling to mine imbalanced data. The 6th International Conference on Machine Learning and Applications:363–368.
  • Hart, P.E. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14:515–516.
  • Li, Y., Qin, Y., Xie, Y., Tian, F. (2013). Grouped penalization estimation of the osteoporosis data in the Traditional Chinese Medicine. Journal of Applied Statistics 40(4):699–711.
  • Huang, J., Ma, S., Xie, H., Zhang, C.-H. (2009). A group bridge approach for variable selection. Biometrika 96:339–355.
  • Ma, S., Huang, J. (2007). Combining multiple markers for classification using ROC. Biometrics 63:751–757.
  • Ma, S., Huang, J. (2008). Penalized feature selection and classification in bioinformatics. Briefings of Bioinformatics 9:392–403.
  • Marzban, C. (2004). The ROC curve and the area under it as performance measure. Weather and Forecasting 19:1106–1114.
  • Meier, L., Geer, S.V. D., Buhlmann, P. (2008). The group lasso for logistic regression. Journal of Royal Statistical Society: Series B 70:53–71.
  • Pepe, M.S., Cai, T., Longton, G. (2006). Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics 62:221–229.
  • Sun, Y., Kamel M.S., Wong A.K. C., et al. (2007). Cost-sensitive boosting for classification of imbalance data. Pattern Recognition 40:3358–3378.
  • Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of Royal Statistical Society: Series B 58:267–288.
  • Tibshirani, R. (2011). Regression shrinkage and selection via the lasso: A retrospective. Journal of Royal Statistical Society: Series B 73:273–282.
  • Wang, L., Zhao, X., Wu, X., et al. (2012). Diagnosis analysis of 4 TCM patterns in suboptimal health status: A structural equation modeling approach. Evidence-Based Complementary and Alternative Medicine, doi: 10.1155/2012/970985.
  • Wang, Q. (2001). Traditional Chinese Medicine will make new contributions to mankind in treating sub health conditions in the 21th century. Journal of Beijing University of Traditional Chinese Medicine 24(2):1–4.
  • Weiss, G.M., Provost, F. (2003). Learning when training data are costly: The effect of class distribution on tree induction. Journal of Artificial Intelligence Research 19:315–354.
  • Yan, Y.X., Liu, Y.Q., Li, M., et al. (2009). Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. Journal of Epidemiology 19(6):333–341.
  • Yuan, M., Lin, Y. (2006). Model selection and estimation in regression with grouped variables. Journal of Royal Statistical Society: Series B 68:49–67.
  • Zhang, B., Fang, G. (2012). A review of dimensional deduction and variable selection for high dimensional panel data. Statistics and Information Forum 27(6):21–28.
  • Zhou, H., Alexander, D.H., Sehl, M.E., Sinsheimer, J.S., Sobel, E.M., Lange, K. (2011). Penalized regression for genome-wide association screening of sequence data. Proceeding of the Pacific Symposium on Biocomputing 106–117.
  • Zhou, X.H., Chen, B., Xie, Y.M., et al. (2010). Variable selection using the optimal ROC curve: An application to a traditional Chinese medicine study on osteoporosis disease. Statistics in Medicine 31:628–635.
  • Zhou, X.H., Obuchowski, N.A., McClish, D.K. (2002). Statistical Methods in Diagnostic Medicine. New York: Wiley
  • Zhou, Z., Liu, X. (2006). Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans Knowledge Data Engineering 18(1):63–77.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.