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Multi-step methods for choosing the best set of variables in regression analysis

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  • Hiroshi Konno
  • Yoshihiro Takaya

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  • Hiroshi Konno & Yoshihiro Takaya, 2010. "Multi-step methods for choosing the best set of variables in regression analysis," Computational Optimization and Applications, Springer, vol. 46(3), pages 417-426, July.
    • Handle: RePEc:spr:coopap:v:46:y:2010:i:3:p:417-426
    DOI: 10.1007/s10589-008-9193-6
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    References listed on IDEAS

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    1. Galindo, J & Tamayo, P, 2000. "Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications," Computational Economics, Springer;Society for Computational Economics, vol. 15(1-2), pages 107-143, April.
    2. Hiroshi Konno & Hiroaki Yamazaki, 1991. "Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market," Management Science, INFORMS, vol. 37(5), pages 519-531, May.
    3. Harlan Crowder & Ellis L. Johnson & Manfred Padberg, 1983. "Solving Large-Scale Zero-One Linear Programming Problems," Operations Research, INFORMS, vol. 31(5), pages 803-834, October.
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    Cited by:

    1. Miyashiro, Ryuhei & Takano, Yuichi, 2015. "Mixed integer second-order cone programming formulations for variable selection in linear regression," European Journal of Operational Research, Elsevier, vol. 247(3), pages 721-731.
    2. Toshiki Sato & Yuichi Takano & Ryuhei Miyashiro & Akiko Yoshise, 2016. "Feature subset selection for logistic regression via mixed integer optimization," Computational Optimization and Applications, Springer, vol. 64(3), pages 865-880, July.
    3. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.

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