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Journal of Quality Technology
A Quarterly Journal of Methods, Applications and Related Topics
Volume 51, 2019 - Issue 1
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RESEARCH ARTICLE

A new variable selection method based on SVM for analyzing supersaturated designs

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References

  • Abraham, B., H. Chipman, and K. Vijayan. 1999. Some risks in the construction and analysis of supersaturated designs. Technometrics 41 (2):135–41.
  • Belloni, A., V. Chernozhukov, and L. Wang. 2011. Square-root lasso: Pivotal recovery of sparse signals via conic programming. Biometrika 98:791–806.
  • Booth, K. H. V., and D. R. Cox. 1962. Some systematic supersaturated designs. Technometrics 4 (4):489–95.
  • Box, G. E. P., and R. D. Meyer. 1986. Analysis for unreplicated fractional factorials. Technometrics 28 (1):11–8.
  • Bulutoglu, D. A. 2007. Cyclicly constructed E(s2)-optimal supersaturated designs. Journal of Statistical Planning and Inference 137:2413–28.
  • Candes, E. J., and T. Tao. 2007. The Dantzig selector: Statistical estimation when p is much larger than n. The Annals of Statistics 35 (6):2313–51.
  • Chang, C.-C., and C.-J. Lin. 2013. LibSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
  • Chen, R. B., J. Z. Weng, and C. H. Chu. 2013. Screening procedure for supersaturated designs using a Bayesian variable selection method. Quality and Reliability Engineering International 29:89–101.
  • Cherkassky, V. and F. Mulier. 1998. Learning from data: Concepts, theory, and methods. (Adaptive and cognitive dynamic systems: Signal processing, learning, communications, and control.) New York, NY: John Wiley & Sons.
  • Cortes, C., and V. Vapnik. 1995. Support vector networks. Machine Learning 20 (3):273–97.
  • Das, U., S. Gupta, and S. Gupta. 2014. Screening active factors in supersaturated designs. Computational Statistics and Data Analysis 77:223–32.
  • Dejaegher, B., and Y. Vander Heyden. 2008. Supersaturated designs: Set-ups, data interpretation, and analytical applications. Annals of Analytical and Bioanalytical Chemistry 390 (5):1227–40.
  • Drosou, K., C. Koukouvinos, and A. Lappa. 2017. Screening active effects in supersaturated designs with binary response via control charts. Quality and Reliability Engineering International 33 (7):1475–83. doi: 10.1002/qre.2119
  • Drucker, H., C. J. C. Burges, L. Kaufman, A. Smola, and V. Vapnik. 1997. Support vector regression machines. Advances in Neural Information Processing Systems 9:155–61.
  • Duan, K., and J. C. Rajapakse. 2005. SVM-RFE peak selection for cancer classification with mass spectrometry data. In Proceedings of the Third Asia-Pacific-Bioinformatics-Conference (APBC), ed. by Y.-P. Phoebe Chen and L. Wong, 191–200. Singapore: World Scientific.
  • Errore, A., B. Jones, W. Li, and C. J. Nachtsheim. 2017. Using definitive screening designs to identify active first- and second-order factor effects. Journal of Quality Technology 49 (3):244–64.
  • Fang, K. T., G. Ge, M. Q. Liu, and H. Qin. 2004. Combinatorial constructions for optimal supersaturated designs. Discrete Math 279 (1–3):191–202.
  • Fang, K. T., D. K. J. Lin, and M. Q. Liu. 2003. Optimal mixed-level supersaturated design. Metrika 58 (3):279–91.
  • Fang, J., and D. Tai. 2011. Evaluation of mutual information, genetic algorithm and SVR for feature selection in QSAR regression. Current Drug Discovery Technologies 8 (2):107–11.
  • Georgiou, S. D. 2014. Supersaturated designs: A review of their construction and analysis. Journal of Statistical Planning and Inference 144:92–109.
  • Guyon, I., J. Weston, S. Barnhill, and V. Vapnik. 2002. Gene selection for cancer classification using support vector machines. Machine Learning 46 (1/3):389–422.
  • Holcomb, D., D. Montgomery, and W. Carlyle. 2003. Analysis of supersaturated designs. Journal of Quality Technology 35 (1):13–27.
  • Huang, M.-L., Y.-H. Hung, W. M. Lee, R. K. Li, and B.-R. Jiang. 2014. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. Scientific World Journal 2014:1–10.
  • Huang, T.-M., V. Kecman, and I. Kopriva. 2006. Kernel based algorithms for mining huge data sets. Berlin, Heidelberg, Germany: Springer-Verlag.
  • Huang, H., J. Yang, and M.-Q. Liu. 2014. Functionally induced priors for component wise Gibbs sampler in the analysis of supersaturated designs. Computational Statistics and Data Analysis 72:1–12.
  • Jones, B., D. K. J. Lin, and C. J. Nachtsheim. 2008. Bayesian D-optimal supersaturated designs. Journal of Statistical Planning and Inference 138 (1):86–92.
  • Jones, B., and D. Majumdar. 2014. Optimal supersaturated designs. Journal of the American Statistical Association 109 (508):1592–600.
  • Jones, B., and C. J. Nachtsheim. 2011. A class of three-level designs for definitive screening in the presence of second-order effects. Journal of Quality Technology 43 (1):1–14.
  • Koukouvinos, C., K. Mylona, and D. E. Simos. 2008. E(s2)Optimal and minimax-optimal cyclic supersaturated designs via multi-objective simulated annealing. Journal of Statistical Planning and Inference 138 (6):1639–46.
  • Li, P. F., M. Q. Liu, and R. C. Zhang. 2004. Some theory and the construction of mixed-level supersaturated designs. Statistics & Probability Letters 69 (1):105–16.
  • Li, P., S. L. Zhao, and R. C. Zhang. 2010. A cluster analysis selection strategy for supersaturated designs. Computational Statistics and Data Analysis 54 (6):1605–12.
  • Li, R., and D. K. J. Lin. 2002. Data analysis in supersaturated designs. Statistics and Probability Letters 59 (2):135–44.
  • Li, X., T. Zhao, X. Yuan, and H. Liu. 2015. The flare package for high dimensional linear regression and precision matrix estimation in R. Journal of Machine Learning Research 16:553–7.
  • Lin, D. K. J. 1993. A new class of supersaturated designs. Technometrics 35 (1):28–31.
  • Lin, D. K. J. 1995. Generating systematic supersaturated designs. Technometrics 37 (2):213–25.
  • Liu, M. Q., K. T. Fang, and F. J. Hickernell. 2006. Connections among different criteria for asymmetrical fractional factorial designs. Statistica Sinica 16 (4):1285–97.
  • Liu, Y., and M. Q. Liu. 2011. Construction of optimal supersaturated design with large number of levels. Journal of Statistical Planning and Inference 141 (6):2035–43.
  • Liu, Y., and M. Q. Liu. 2012. Construction of equidistant and weak equidistant supersaturated designs. Metrika 75 (1):33–53.
  • Liu, Y., and M. Q. Liu. 2013. Construction of supersaturated design with large number of factors by the complementary design method. Acta Mathematicae Applicatae Sinica, English Series 29 (2):253–62.
  • Liu, X., and J. Tang. 2014. Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. Systems Journal IEEE 8 (3):910–20.
  • Lu, X., and X. Wu. 2004. A strategy of searching active factors in supersaturated screening experiments. Journal of Quality Technology 36 (4):392–9.
  • Marley, C. J., and D. C. Woods. 2010. A comparison of design and model selection methods for supersaturated experiments. Computational Statistics and Data Analysis 54 (12):3158–67.
  • Mundra, P. A., and J. C. Rajapakse. 2007. SVM-RFE with relevancy and redundancy criteria for gene selection. In Chapter in pattern recognition in bioinformatics, ed. by J. C. Rajapakse, B. Schmidt, and L.G. Volkert, vol. 4774, 242–52. Heidelberg, Germany: Springer.
  • Nguyen, N. K. 1996. An algorithmic approach to constructing supersaturated designs. Technometrics 38 (1):69–73.
  • Nguyen, N. K., and C. S. Cheng. 2008. New E(s2)-optimal supersaturated designs constructed from incomplete block designs. Technometrics 50:26–31.
  • Phoa, F. K. H., Y.-H. Pan, and H. Xu. 2009. Analysis of supersaturated designs via the Dantzig selector. Journal of Statistical Planning and Inference 139 (7):2362–72. Vol.
  • Samb, M. L., F. Camara, S. Ndiaye, Y. Slimani, and M. A. Esseghir. 2012. A novel RFE-SVM-based feature selection approach for classification. International Journal of Advanced Science and Technology 43:27–36.
  • Satterthwaite, F. 1959. Random balance experimentation. Technometrics 1 (2):111–37.
  • Schlkopf, B., C. J. C. Burges, and A. J. Smola. 1999. Advances in kernel methods: Support vector learning. Cambridge, MA: MIT Press.
  • Smola, A. J., and B. Schölkopf. 2004. A tutorial on support vector regression. Statistics and Computing 14 (3):199–222.
  • Tang, B., and C. F. J. Wu. 1997. A method for constructing supersaturated designs and its E(s2) optimality. Canadian Journal of Statistics 25:191–201.
  • Tang, Y., Y.-Q. Zhang, and Z. Huang. 2007. Development of two-stage SVM-RFE gene selection strategy for microarray expression data analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics 4 (3):365–81.
  • Vapnik, V. 2000. The nature of statistical learning theory. 2nd ed. New York, NY: John Wiley and Sons, Inc.
  • Weese, M. L., B. J. Smucker, and D. J. Edwards. 2015. Searching for powerful supersaturated designs. Journal of Quality Technology 47 (1):66–84.
  • Westfall, P. H., S. S. Young, and D. K. J. Lin. 1998. Forward selection error control in the analysis of supersaturated designs. Statistica Sinica 8:101–17.
  • Williams, K. R. 1968. Designed experiments. Rubber Age 100:65–71.
  • Wu, C. 1993. Construction of supersaturated designs through partially aliased interactions. Biometrika 80 (3):661–9.
  • Wu, C. F. J., and M. Hamada. 2000. Experiments: Planning, analysis, and parameter design optimization. 2nd ed. New York, NY: John Wiley & Sons, Inc.
  • Yamada, S., and D. K. J. Lin. 2002. Construction of mixed-level supersaturated design. Metrika 56 (3):205–14.
  • Yamada, S., and T. Matsui. 2002. Optimality of mixed-level supersaturated designs. Journal of Statistical Planning and Inference 104 (2):459–68.
  • Yamada, S., M. Matsui, T. Matsui, D. K. J. Lin, and T. Takahashi. 2006. A general construction method for mixed-level supersaturated design. Computational Statistics & Data Analysis 50 (1):254–65.
  • Yan, K., and D. Zhang. 2015. Feature selection and analysis on correlated gas sensor data with recursive feature elimination. Sensors and Actuators B: Chemical 212:353–63.
  • Yin, Y. H., Q. Z. Zhang, and M. Q. Liu. 2013. A two-stage variable selection strategy for supersaturated designs with multiple responses. Frontiers of Mathematics in China 8 (3):717–30.
  • Zhang, Q. Z., R. C. Zhang, and M. Q. Liu. 2007. A method for screening active effect in supersaturated designs. Journal of Statistical Planning and Inference 137 (6):2068–79.

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