References
- Kubat M, Matwin S. Addressing the curse of imbalanced training sets: one-sided selection. ICML, vol. 97; 1997; Nashville, USA. p. 179–186.
- Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2012;16:321–357.
- Qu HN, Li GZ, Xu WS. An asymmetric classifier based on partial least squares. Pattern Recognit. 2010;43:3448–3457. doi: 10.1016/j.patcog.2010.05.002
- Sun Y, Kamel MS, Wong AK, Wang Y. Cost-sensitive boosting for classification of imbalanced data. Pattern Recognit. 2007;40:3358–3378. doi: 10.1016/j.patcog.2007.04.009
- Wu G, Chang EY. Class-boundary alignment for imbalanced dataset learning. ICML; 2003; Washington, DC. p. 49–56.
- Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 1997;30:1145–1159. doi: 10.1016/S0031-3203(96)00142-2
- Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3:32–35. doi: 10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3
- Qiao X, Liu Y. Adaptive weighted learning for unbalanced multicategory classification. Biometrics. 2009;65: 159–168. doi: 10.1111/j.1541-0420.2008.01017.x
- Li Y, Zhu JF, Shia B-C. Methodologies for variable selection in data mining and application in health food market research. SIF, vol. 28; 2013. p. 17–25.
- Lin WJ, Chen JJ. Class-imbalanced classifiers for high-dimensional data. Brief. Bioinform. 2013;14:13–26. doi: 10.1093/bib/bbs006
- Yan YX, Liu YQ, Li M, Hu PF, Guo AM, Yang XH, Qiu JJ, Yang SS, Shen J, Zhang LP, Wang W. Development and evaluation of a questionnaire for measuring suboptimal health status in urban Chinese. J Epidemiol. 2009;19: 333–341. doi: 10.2188/jea.JE20080086
- Wang Q. Traditional Chinese medicine will make new contributions to mankind in treating sub health conditions in the 21st century. J Beijing Univ TCM. 2001;24:1–4.
- Wang LM, Zhao X, Wu XL, Li Y, Yi DH, Cui HT, Chen JX. Diagnosis analysis of 4 TCM patterns in suboptimal health status: a structural equation modelling approach. Evid Based Complement Altern Med. 2012;2012:970985.
- Tibshirani R. Regression shrinkage and selection via the lasso. J R Stat Soc Ser B. 1996;58:267–288.
- Tibshirani R. Regression shrinkage and selection via the lasso: a retrospective. J R Stat Soc Ser B. 2011;73:273–282. doi: 10.1111/j.1467-9868.2011.00771.x
- Ma S, Huang J. Penalized feature selection and classification in bioinformatics. Brief. Bioinform. 2008;9:392–403. doi: 10.1093/bib/bbn027
- Huang J, Ma S, Xie H, Zhang CH. A group bridge approach for variable selection. Biometrika. 2009;96:339–355. doi: 10.1093/biomet/asp020
- Yuan M, Lin Y. Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B. 2006;68:49–67. doi: 10.1111/j.1467-9868.2005.00532.x
- Meier L, Van De Geer S, Bühlmann P. The group lasso for logistic regression. J R Stat Soc Ser B. 2008;70:53–71. doi: 10.1111/j.1467-9868.2007.00627.x
- Zhou H, Alexander DH, Sehl ME, Sinsheimer JS, Sobel EM, Lange K. Penalized regression for genome-wide association screening of sequence data. Pac Symp Biocomput. 2011; 106–117.
- Kubat M, Holte R, Matwin S. Learning when negative examples abound. In: van Someren M, Gerhard W, editors. ECML. Berlin: Springer; 1997. p. 146–153.