杜海鹏等：基于多目标支持向量机的ADHD分类 447 Connect,2011,1(1):13 [20]Askan A,Sayin S.SVM classification for imbalanced data sets [17]Reris R,Brooks J P.Principal component analysis and using a multiobjective optimization framework.Ann Oper Res, optimization:a tutorial /Proceedings of 14th INFORMS 2014,216(1):191 Computing Sociery Conference,Richmond,Virginia,US,2015: [21]Das I,Dennis J E.Normal-boundary intersection:a new method 212 for generating the Pareto surface in nonlinear multicriteria [18]Cortes C,Vapnik V.Support-vector networks.Mach Learn,1995, optimization problems.SIAMJOprim,1998.8(3):631 20(3):273 [22]Breiman L.Random forests.Mach Learn,2001,45(1):5 [19]Aytug H,Say S.Exploring the trade-off between generalization [23]Peng X L.Lin P.Zhang T S,et al.Extreme learning machine- and empirical errors in a one-norm SVM.Eur J Oper Res,2012. based classification of ADHD using brain structural MRI data. 218(3):667 P1 OS One,2013,8(11):e79476Connect, 2011, 1（1）: 13 Reris R, Brooks J P. Principal component analysis and optimization: a tutorial // Proceedings of 14th INFORMS Computing Society Conference, Richmond, Virginia, US, 2015: 212 [17] Cortes C, Vapnik V. Support-vector networks. Mach Learn, 1995, 20（3）: 273 [18] Aytug H, Sayın S. Exploring the trade-off between generalization and empirical errors in a one-norm SVM. Eur J Oper Res, 2012, 218（3）: 667 [19] Aşkan A, Sayın S. SVM classification for imbalanced data sets using a multiobjective optimization framework. Ann Oper Res, 2014, 216（1）: 191 [20] Das I, Dennis J E. Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM J Optim, 1998, 8（3）: 631 [21] [22] Breiman L. Random forests. Mach Learn, 2001, 45（1）: 5 Peng X L, Lin P, Zhang T S, et al. Extreme learning machine￾based classification of ADHD using brain structural MRI data. PloS One, 2013, 8（11）: e79476 [23] 杜海鹏等： 基于多目标支持向量机的 ADHD 分类 · 447 ·