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ChaoTon Su, YuHsiang Hsiao, "Multiclass MTS for Simultaneous Feature Selection and Classification," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 2, pp. 192205, February, 2009.  
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@article{ 10.1109/TKDE.2008.128, author = {ChaoTon Su and YuHsiang Hsiao}, title = {Multiclass MTS for Simultaneous Feature Selection and Classification}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {21}, number = {2}, issn = {10414347}, year = {2009}, pages = {192205}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.128}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  Multiclass MTS for Simultaneous Feature Selection and Classification IS  2 SN  10414347 SP192 EP205 EPD  192205 A1  ChaoTon Su, A1  YuHsiang Hsiao, PY  2009 KW  Classification KW  feature selection KW  multiclass problem KW  MahalanobisTaguchi system (MTS) KW  weighted Mahalanobis distance KW  GramSchmidt orthogonalization process KW  gestational diabetes mellitus. VL  21 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] G. Taguchi, S. Chowdhury, and Y. Wu, The MahalanobisTaguchi System. McGrawHill, 2001.
[2] G. Taguchi and R. Jugulum, The MahalanobisTaguchi Strategy. John Wiley & Sons, 2002.
[3] J. Srinivasaraghavan and V. Allada, “Application of Mahalanobis Distance as a Lean Assessment Metric,” Int'l J. Advanced Manufacturing Technology, vol. 29, pp. 11591168, 2006.
[4] T. Riho, A. Suzuki, J. Oro, K. Ohmi, and H. Tanaka, “The Yield Enhancement Methodology for Invisible Defects Using the MTS+ Method,” IEEE Trans. Semiconductor Manufacturing, vol. 18, no. 4, pp. 561568, 2005.
[5] P. Das and S. Datta, “Exploring the Effects of Chemical Composition in Hot Rolled Steel Product Using Mahalanobis Distance Scale under MahalanobisTaguchi System,” Computational Materials Science, vol. 38, no. 4, pp. 671677, 2007.
[6] G. Taguchi, S. Chowdhury, and Y. Wu, Taguchi's Quality Engineering Handbook. Wiley, 2005.
[7] C.T. Su and Y.H. Hsiao, “An Evaluation of the Robustness of MTS for Imbalanced Data,” IEEE Trans. Knowledge and Data Eng., vol. 19, no. 10, pp. 13211332, Oct. 2007.
[8] Y.L. Cun, B. Boser, J. Denker, D. Hendersen, R. Howard, W. Hubbard, and L. Jackel, “Backpropagation Applied to Handwritten Zip Code Recognition,” Neural Computation, vol. 1, pp.541551, 1989.
[9] O. Chapelle, P. Haffner, and V.N. Vapnik, “Support Vector Machines for HistogramBased Image Classification,” IEEE Trans. Neural Networks, vol. 10, no. 5, pp. 10551064, 1999.
[10] M. Shami and W. Verhelst, “An Evaluation of the Robustness of Existing Supervised Machine Learning Approaches to the Classification of Emotions in Speech,” Speech Comm., vol. 49, no. 3, pp. 201212, 2007.
[11] S. Thamarai Selvi, S. Arumugam, and L. Ganesan, “BIONET: An Artificial Neural Network Model for Diagnosis of Diseases,” Pattern Recognition Letters, vol. 21, no. 8, pp. 721740, 2000.
[12] W. Lam, M. Ruiz, and P. Srinivasan, “Automatic Text Categorization and Its Application to Text Retrieval,” IEEE Trans. Knowledge and Data Eng., vol. 11, no. 6, pp. 865879, Nov./Dec. 1999.
[13] C.W.D. Justin and R.J. Victor, “Feature Subset Selection with a Simulated Annealing Data Mining Algorithm,” J. Intelligent Information Systems, vol. 9, pp. 5781, 1997.
[14] B. Walczk and D.L. Massart, “Rough Sets Theory,” Chemometrics and Intelligent Laboratory Systems, vol. 47, pp. 116, 1999.
[15] R.A. Johnson and D.W. Wichern, Applied Multivariate Statistical Analysis. PrenticeHall, 1998.
[16] H. Kim and G.J. Koehler, “Theory and Practice of Decision Tree Induction,” Omega, vol. 23, no. 6, pp. 637652, 1995.
[17] B. Schölkopf and A.J. Smola, Learning with Kernels. The MIT Press, 2002.
[18] C.W. Hsu and C.J. Lin, “A Comparison of Methods for Multiclass Support Vector Machines,” IEEE Trans. Neural Networks, vol. 13, no. 2, pp. 415425, 2002.
[19] M.E. Tipping, “Sparse Bayesian Learning and the Relevance Vector Machine,” J. Machine Learning Research, vol. 1, pp. 211244, 2001.
[20] I.W. Tsang, J.T. Kwok, and P.M. Cheung, “Core Vector Machines: Fast SVM Training on Very Large Data Sets,” J. Machine Learning Research, vol. 6, pp. 363392, 2005.
[21] D.W. Patterson, Artificial Neural Networks: Theory and Applications. Prentice Hall, 1996.
[22] J. Weston and C. Watkins, “MultiClass Support Vector Machines,” Technical Report CSDTR9804, London, Egham, TW20 0EX, UK, 1998.
[23] V.N. Vapnik, Statistical Learning Theory. Wiley, 1998.
[24] S. Asharaf, M.N. Murty, and S.K. Shevade, “Multiclass Core Vector Machine,” Proc. 24th Int'l Conf. Machine Learning (ICML), 2007.
[25] H. Zhang and J. Malik, “Selecting Shape Features Using MultiClass Relevance Vector Machine,” Technical Report UCB/EECS20056, Dept. of Electrical Eng. and Computer Sciences, Univ. of California, Berkeley, 2005.
[26] U.H.G. KreBel, “Pairwise Classification and Support Vector Machines,” Advances in Kernel Methods: Support Vector Learning. pp. 255268, MIT Press, 1999.
[27] G. Ou and Y.L. Murphey, “MultiClass Pattern Classification Using Neural Networks,” Pattern Recognition, vol. 40, no. 1, pp. 418, 2007.
[28] T.G. Dietterich and G. Bakiri, “Solving Multiclass Learning Problem via ErrorCorrecting Output Codes,” J. Artificial Intelligence Research, vol. 2, pp. 263286, 1995.
[29] J. Wu, J.G. Zhou, and P.L. Yan, “Incremental Proximal Support Vector Classifier for MultiClass Classification,” Proc. Int'l Conf. Machine Learning and Cybernetics (ICMLC '04), vol. 5, pp. 32013206, 2004.
[30] Y. Tian, Z. Qi, and N. Deng, “A New Support Vector Machine for MultiClass Classification,” Proc. Fifth Int'l Conf. Computer and Information Technology (ICCIT '05), pp. 1822, 2005.
[31] R. Anand, K. Mehrotra, C.K. Mohan, and S. Ranka, “Efficient Classification for Multiclass Problems Using Modular Neural Networks,” IEEE Trans. Neural Networks, vol. 6, no. 1, pp. 117124, 1995.
[32] R. Duda, P. Hart, and D. Stork, Pattern Classification. Wiley, 2001.
[33] F. Masulli and G. Valentini, “Effectiveness of Error Correcting Output Codes in Multiclass Learning Problems,” LNCS 1857, pp.107116, 2000.
[34] W.H. Woodall, R. Koudelik, K.L. Tsui, S.B. Kim, Z.G. Stoumbos, and C.P. Carvounis, “A Review and Analysis of the MahalanobisTaguchi System,” Technometrics, vol. 45, no. 1, pp. 115, 2003.
[35] A. Kalousis, J. Prados, and M. Hilario, “Stability of Feature Selection Algorithms,” Proc. Fifth IEEE Int'l Conf. Data Mining (ICDM), 2005.
[36] T.K. Ho and M. Basu, “Complexity Measures of Supervised Classification Problems,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 289300, Mar. 2002.
[37] C.C. Chang and C.J. Lin, LIBSVM: A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlinlibsvm, 2001.
[38] R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” J. Royal Statistical Soc. Series B, vol. 58, no. 1, pp. 267288, 1996.
[39] B.E. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, “Least Angle Regression,” The Annals of Statistics, vol. 32, pp. 407451, 2004.
[40] I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene Selection for Cancer Classification Using Support Vector Machines,” Machine Learning, vol. 46, pp. 389422, 2002.
[41] A. Rakotomamonjy, “Variable Selection Using SVMbased Criteria,” J. Machine Learning Research, vol. 3, pp. 13571370, 2003.
[42] L. Shih, J.D.M. Rennie, Y.H. Chang, and D.R. Karger, “Text Bundling: StatisticsBased Data Reduction,” Proc. 20th Int'l Conf. Machine Learning (ICML), 2003.
[43] X.B. Li, “Data Reduction via Adaptive Sampling,” Comm. Information and Systems, vol. 2, no. 1, pp. 5368, 2002.
[44] H. Liu and H. Mtotda, Instance Selection and Construction for Data Mining. Kluwer Academic Publishers, 2001.
[45] N.H. Cho, H.C. Jang, H.K. Park, and Y.W. Cho, “Waist Circumference Is the Key Risk Factor for Diabetes in Korean Women with History of Gestational Diabetes,” Diabetes Research and Clinical Practice, vol. 71, no. 2, pp. 177183, 2006.
[46] M.K. Barger and M. BidgoodWilson, “Caring for a Woman at High Risk for Type 2 Diabetes,” J. Midwifery and Women's Health, vol. 51, no. 3, pp. 222226, 2006.
[47] B.E. Metzger, N.H. Cho, S.M. Rston, and R. Radvany, “Pregnancy Weight and Antepartum Insulin Secretion Predict Glucose Tolerance Five Years after Gestational Diabetes Mellitus,” Diabetes Care, vol. 16, pp. 15981605, 1993.
[48] S.L. Kjos, R.K. Peters, A. Xiang, O.A. Henry, M. Montoro, and T.A. Buchanan, “Predicting Future Diabetes in Latino Women with Gestational Diabetes,” Diabetes, vol. 44, pp. 586591, 1995.