메뉴 건너뛰기




Volumn 247, Issue 3, 2015, Pages 721-731

Mixed integer second-order cone programming formulations for variable selection in linear regression

Author keywords

Information criterion; Integer programming; Multiple linear regression; Second order cone programming; Variable selection

Indexed keywords

LINEAR REGRESSION; SET THEORY;

EID: 84940603932     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2015.06.081     Document Type: Article
Times cited : (84)

References (45)
  • 1
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • Akaike H. A new look at the statistical model identification IEEE Transactions on Automatic Control 19 1974 716 723
    • (1974) IEEE Transactions on Automatic Control , vol.19 , pp. 716-723
    • Akaike, H.1
  • 3
    • 84886567160 scopus 로고    scopus 로고
    • University of California, School of Information and Computer Science Irvine, CA
    • Bache K., and Lichman M. UCI machine learning repository 2013 University of California, School of Information and Computer Science Irvine, CA
    • (2013) UCI machine learning repository
    • Bache, K.1    Lichman, M.2
  • 6
    • 84994196386 scopus 로고    scopus 로고
    • Mixed-integer second-order cone programming: A survey
    • Topaloglu H. INFORMS Catonsville, MD
    • Benson H.Y., and Saʇram Ü. Mixed-integer second-order cone programming: A survey Topaloglu H. Tutorials in operations research 2013 INFORMS Catonsville, MD 13 36
    • (2013) Tutorials in operations research , pp. 13-36
    • Benson, H.Y.1    Saʇram, Ü.2
  • 9
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • Blum A.L., and Langley P. Selection of relevant features and examples in machine learning Artificial Intelligence 97 1997 245 271
    • (1997) Artificial Intelligence , vol.97 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 13
    • 80053452142 scopus 로고    scopus 로고
    • Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection
    • Das A., and Kempe D. Submodular meets spectral: Greedy algorithms for subset selection, sparse approximation and dictionary selection Proceedings of the twenty eighth international conference on machine learning 2011 1057 1064
    • (2011) Proceedings of the twenty eighth international conference on machine learning , pp. 1057-1064
    • Das, A.1    Kempe, D.2
  • 14
    • 29144477149 scopus 로고    scopus 로고
    • An efficient support vector machine learning method with second-order cone programming for large-scale problems
    • Debnath R., Muramatsu M., and Takahashi H. An efficient support vector machine learning method with second-order cone programming for large-scale problems Applied Intelligence 23 2005 219 239
    • (2005) Applied Intelligence , vol.23 , pp. 219-239
    • Debnath, R.1    Muramatsu, M.2    Takahashi, H.3
  • 15
    • 0001878035 scopus 로고
    • Multiple regression analysis
    • Ralston A. Wilf H.S. John Wiley & Sons New York, NY
    • Efroymson M.A. Multiple regression analysis Ralston A. Wilf H.S. Mathematical methods for digital computers 1960 John Wiley & Sons New York, NY 191 203
    • (1960) Mathematical methods for digital computers , pp. 191-203
    • Efroymson, M.A.1
  • 17
    • 0016128505 scopus 로고
    • Regressions by leaps and bounds
    • Furnival G.M., and Wilson R.W. Jr. Regressions by leaps and bounds Technometrics 16 1974 499 511
    • (1974) Technometrics , vol.16 , pp. 499-511
    • Furnival, G.M.1    Wilson, R.W.2
  • 21
    • 0017280570 scopus 로고
    • The analysis and selection of variables in linear regression
    • Hocking R.R. The analysis and selection of variables in linear regression Biometrics 32 1976 1 49
    • (1976) Biometrics , vol.32 , pp. 1-49
    • Hocking, R.R.1
  • 22
    • 84942484786 scopus 로고
    • Ridge regression: Biased estimation for non-orthogonal problems
    • Hoerl A.E., and Kennard R.W. Ridge regression: Biased estimation for non-orthogonal problems Technometrics 20 1970 55 67
    • (1970) Technometrics , vol.20 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 23
    • 34548210088 scopus 로고    scopus 로고
    • Efficient algorithms for computing the best subset regression models for large-scale problems
    • Hofmann M., Gatu G., and Kontoghiorghes E.J. Efficient algorithms for computing the best subset regression models for large-scale problems Computational Statistics & Data Analysis 52 2007 16 29
    • (2007) Computational Statistics & Data Analysis , vol.52 , pp. 16-29
    • Hofmann, M.1    Gatu, G.2    Kontoghiorghes, E.J.3
  • 25
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • Kohavi R., and John G.H. Wrappers for feature subset selection Artificial Intelligence 97 1997 273 324
    • (1997) Artificial Intelligence , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 26
    • 77954952366 scopus 로고    scopus 로고
    • Multi-step methods for choosing the best set of variables in regression analysis
    • Konno H., and Takaya Y. Multi-step methods for choosing the best set of variables in regression analysis Computational Optimization and Applications 46 2010 417 426
    • (2010) Computational Optimization and Applications , vol.46 , pp. 417-426
    • Konno, H.1    Takaya, Y.2
  • 27
    • 67349161084 scopus 로고    scopus 로고
    • Choosing the best set of variables in regression analysis using integer programming
    • Konno H., and Yamamoto R. Choosing the best set of variables in regression analysis using integer programming Journal of Global Optimization 44 2009 272 282
    • (2009) Journal of Global Optimization , vol.44 , pp. 272-282
    • Konno, H.1    Yamamoto, R.2
  • 32
    • 31144448615 scopus 로고    scopus 로고
    • Using simulated annealing to optimize the feature selection problem in marketing applications
    • Meiri R., and Zahavi J. Using simulated annealing to optimize the feature selection problem in marketing applications European Journal of Operational Research 171 2006 842 858
    • (2006) European Journal of Operational Research , vol.171 , pp. 842-858
    • Meiri, R.1    Zahavi, J.2
  • 35
    • 84940589205 scopus 로고    scopus 로고
    • Feature subset selection for logistic regression via mixed integer optimization
    • Department of Policy and Planning Sciences, University of Tsukuba
    • Sato T., Takano Y., Miyashiro R., and Yoshise A. Feature subset selection for logistic regression via mixed integer optimization Discussion Paper Series, No. 1324 2015 Department of Policy and Planning Sciences, University of Tsukuba
    • (2015) Discussion Paper Series, No. 1324
    • Sato, T.1    Takano, Y.2    Miyashiro, R.3    Yoshise, A.4
  • 36
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz G. Estimating the dimension of a model Annals of Statistics 6 1978 461 464
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 39
    • 0032215931 scopus 로고    scopus 로고
    • Comparison of certain MINLP algorithms when applied to a model structure determination and parameter estimation problem
    • Skrifvars H., Leyffer S., and Westerlund T. Comparison of certain MINLP algorithms when applied to a model structure determination and parameter estimation problem Computers & Chemical Engineering 22 1998 1829 1835
    • (1998) Computers & Chemical Engineering , vol.22 , pp. 1829-1835
    • Skrifvars, H.1    Leyffer, S.2    Westerlund, T.3
  • 40
    • 84963178774 scopus 로고
    • Further analysts of the data by Akaike's information criterion and the finite corrections
    • Sugiura N. Further analysts of the data by Akaike's information criterion and the finite corrections Communications in Statistics - Theory and Methods 7 1978 13 26
    • (1978) Communications in Statistics - Theory and Methods , vol.7 , pp. 13-26
    • Sugiura, N.1
  • 43
    • 0000536634 scopus 로고
    • A new formula for predicting the shrinkage of the coefficient of multiple correlation
    • Wherry R.J. A new formula for predicting the shrinkage of the coefficient of multiple correlation The Annals of Mathematical Statistics 2 1931 440 457
    • (1931) The Annals of Mathematical Statistics , vol.2 , pp. 440-457
    • Wherry, R.J.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.