메뉴 건너뛰기




Volumn 38, Issue 5, 2011, Pages 5110-5118

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

Author keywords

Cancer classification; Estimation of distribution algorithms; Logistic regression; Microarray data; Regularization

Indexed keywords

CANCER CLASSIFICATION; ESTIMATION OF DISTRIBUTION ALGORITHMS; LOGISTIC REGRESSION; MICROARRAY DATA; REGULARIZATION;

EID: 79151480613     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2010.09.140     Document Type: Article
Times cited : (55)

References (69)
  • 1
    • 32444433406 scopus 로고    scopus 로고
    • Using principal components for estimating logistic regression with high-dimensional multicollinear data
    • A.M. Aguilera, M. Escabias, and M.J. Valderrama Using principal components for estimating logistic regression with high-dimensional multicollinear data Computational Statistics and Data Analysis 50 2006 1905 1924
    • (2006) Computational Statistics and Data Analysis , vol.50 , pp. 1905-1924
    • Aguilera, A.M.1    Escabias, M.2    Valderrama, M.J.3
  • 2
    • 0033536012 scopus 로고    scopus 로고
    • Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide microarrays
    • U. Alon, N. Barkai, D. Notterman, K. Gish, S. Ybarra, and D. Mack Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide microarrays Proceedings of the National Academy of Sciences USA 96 1999 6745 6750
    • (1999) Proceedings of the National Academy of Sciences USA , vol.96 , pp. 6745-6750
    • Alon, U.1    Barkai, N.2    Notterman, D.3    Gish, K.4    Ybarra, S.5    MacK, D.6
  • 3
    • 0037461021 scopus 로고    scopus 로고
    • Effective dimension reduction methods for tumor classification using gene expression data
    • A. Antoniadis, S. Lambert-Lacroix, and F. Leblanc Effective dimension reduction methods for tumor classification using gene expression data Bioinformatics 19 5 2003 563 570
    • (2003) Bioinformatics , vol.19 , Issue.5 , pp. 563-570
    • Antoniadis, A.1    Lambert-Lacroix, S.2    Leblanc, F.3
  • 4
    • 41549108812 scopus 로고    scopus 로고
    • Algorithms for sparse linear classifier in the massive data setting
    • S. Balakrishnan, and D. Madigan Algorithms for sparse linear classifier in the massive data setting Journal of Machine Learning Research 9 2008 313 337
    • (2008) Journal of Machine Learning Research , vol.9 , pp. 313-337
    • Balakrishnan, S.1    Madigan, D.2
  • 6
    • 33845678003 scopus 로고    scopus 로고
    • Regularization in statistics
    • P.J. Bickel, and B. Li Regularization in statistics Test 15 2006 271 344
    • (2006) Test , vol.15 , pp. 271-344
    • Bickel, P.J.1    Li, B.2
  • 7
    • 1342330535 scopus 로고    scopus 로고
    • Is cross-validation valid for small-sample microarray classification?
    • U.M. Braga-Neto, and E.R. Dougherty Is cross-validation valid for small-sample microarray classification? Bioinformatics 20 2004 374 380
    • (2004) Bioinformatics , vol.20 , pp. 374-380
    • Braga-Neto, U.M.1    Dougherty, E.R.2
  • 8
    • 33750012146 scopus 로고    scopus 로고
    • Gene selection in cancer classification using sparse logistic regression with Bayesian regularization
    • G.C. Cawley, and N. Talbot Gene selection in cancer classification using sparse logistic regression with Bayesian regularization Bioinformatics 22 2006 2348 2355
    • (2006) Bioinformatics , vol.22 , pp. 2348-2355
    • Cawley, G.C.1    Talbot, N.2
  • 10
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets Journal of Machine Learning Research 7 2006 1 30
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 11
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discrimination methods for the classification of tumors using gene expression data
    • S. Dudoit, J. Fridlyand, and T.P. Speed Comparison of discrimination methods for the classification of tumors using gene expression data Journal of the American Statistical Association 97 2002 77 87
    • (2002) Journal of the American Statistical Association , vol.97 , pp. 77-87
    • Dudoit, S.1    Fridlyand, J.2    Speed, T.P.3
  • 12
    • 84950461478 scopus 로고
    • Estimating the error rate of a prediction rule: Improvement on cross-validation
    • B. Efron Estimating the error rate of a prediction rule: Improvement on cross-validation Journal of the American Statistical Association 78 1983 316 331
    • (1983) Journal of the American Statistical Association , vol.78 , pp. 316-331
    • Efron, B.1
  • 14
    • 84878031768 scopus 로고    scopus 로고
    • Statistical challenges with high dimensionality: Feature selection in knowledge discovery
    • Fan, J.; & Li, R. (2006). Statistical challenges with high dimensionality: Feature selection in knowledge discovery. In Proceedings of the Madrid international congress of mathematicians (Vol. III, pp. 595-622).
    • (2006) Proceedings of the Madrid International Congress of Mathematicians , vol.3 , pp. 595-622
    • Fan, J.1    Li, R.2
  • 15
    • 0002178053 scopus 로고
    • Bias reduction of maximum likelihood estimates
    • D. Firth Bias reduction of maximum likelihood estimates Biometrika 80 1993 27 38
    • (1993) Biometrika , vol.80 , pp. 27-38
    • Firth, D.1
  • 16
    • 16344365619 scopus 로고    scopus 로고
    • Classification using partial least squares with penalized logistic regression
    • G. Fort, and S. Lambert-Lacroix Classification using partial least squares with penalized logistic regression Bioinformatics 21 2005 1104 1111
    • (2005) Bioinformatics , vol.21 , pp. 1104-1111
    • Fort, G.1    Lambert-Lacroix, S.2
  • 17
    • 84952149204 scopus 로고
    • A statistical view of some chemometric regression tools
    • I.E. Frank, and J.H. Friedman A statistical view of some chemometric regression tools Technometrics 35 1993 109 148
    • (1993) Technometrics , vol.35 , pp. 109-148
    • Frank, I.E.1    Friedman, J.H.2
  • 19
    • 34047276652 scopus 로고    scopus 로고
    • Asymptotic properties of a double penalized maximum likelihood estimator in logistic regression
    • S. Gao, and J. Shen Asymptotic properties of a double penalized maximum likelihood estimator in logistic regression Statistics and Probability Letters 77 2007 925 930
    • (2007) Statistics and Probability Letters , vol.77 , pp. 925-930
    • Gao, S.1    Shen, J.2
  • 20
    • 34548105186 scopus 로고    scopus 로고
    • Large-scale Bayesian logistic regression for text categorization
    • A. Genkin, D.D. Lewis, and D. Madigan Large-scale Bayesian logistic regression for text categorization Technometrics 49 2007 291 304
    • (2007) Technometrics , vol.49 , pp. 291-304
    • Genkin, A.1    Lewis, D.D.2    Madigan, D.3
  • 21
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • T.R. Golub, D.K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, and J.P. Mesirov Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring Science 286 1999 531 537
    • (1999) Science , vol.286 , pp. 531-537
    • Golub, T.R.1    Slonim, D.K.2    Tamayo, P.3    Huard, C.4    Gaasenbeek, M.5    Mesirov, J.P.6
  • 23
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Machine Learning 46 2002 389 422
    • (2002) Machine Learning , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 24
    • 0020083498 scopus 로고
    • The meaning and use of the area under a receiver operating characteristic (ROC) curve
    • J.A. Hanley, and B.J. McNeil The meaning and use of the area under a receiver operating characteristic (ROC) curve Radiology 143 1982 29 36
    • (1982) Radiology , vol.143 , pp. 29-36
    • Hanley, J.A.1    McNeil, B.J.2
  • 25
    • 15944419638 scopus 로고    scopus 로고
    • Efficient quadratic regularization for expression arrays
    • T. Hastie, and R. Tibshirani Efficient quadratic regularization for expression arrays Biostatistics 5 2004 329 340
    • (2004) Biostatistics , vol.5 , pp. 329-340
    • Hastie, T.1    Tibshirani, R.2
  • 27
    • 84942484786 scopus 로고
    • Ridge regression: Biased estimates for nonorthogonal problems
    • A.E. Hoerl, and R.W. Kennard Ridge regression: Biased estimates for nonorthogonal problems Technometrics 12 1970 55 67
    • (1970) Technometrics , vol.12 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 33
    • 0038636391 scopus 로고    scopus 로고
    • A comparative assessment of classification methods
    • M.Y. Kiang A comparative assessment of classification methods Decision Support Systems 35 2003 441 454
    • (2003) Decision Support Systems , vol.35 , pp. 441-454
    • Kiang, M.Y.1
  • 34
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi, and G.H. John 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
  • 41
  • 43
    • 34548125448 scopus 로고    scopus 로고
    • Logistic regression for disease classification using microarray data: Model selection in a large p and small n case
    • J.G. Liao, and K.-V. Chin Logistic regression for disease classification using microarray data: Model selection in a large p and small n case Bioinformatics 23 2007 1945 1951
    • (2007) Bioinformatics , vol.23 , pp. 1945-1951
    • Liao, J.G.1    Chin, K.-V.2
  • 48
    • 28944437658 scopus 로고    scopus 로고
    • Regularized ROC method for disease classification and biomarker selection with microarray data
    • S. Ma, and J. Huang Regularized ROC method for disease classification and biomarker selection with microarray data Bioinformatics 21 2005 4356 4362
    • (2005) Bioinformatics , vol.21 , pp. 4356-4362
    • Ma, S.1    Huang, J.2
  • 50
    • 4544295413 scopus 로고    scopus 로고
    • Case-control study of binary disease trait considering interactions between SNPs and environmental effects using logistic regression
    • Nakamichi, R. E.; Imoto, S.; & Miyano, S. (2004). Case-control study of binary disease trait considering interactions between SNPs and environmental effects using logistic regression. In Fourth IEEE symposium on bioinformatics and bioengineering (Vol. 21, pp. 73-78).
    • (2004) Fourth IEEE Symposium on Bioinformatics and Bioengineering , vol.21 , pp. 73-78
    • Nakamichi, R.E.1    Imoto, S.2    Miyano, S.3
  • 52
    • 0036166439 scopus 로고    scopus 로고
    • Tumor classification by partial least squares using microarray gene expression data
    • D.V. Nguyen, and D.M. Rocke Tumor classification by partial least squares using microarray gene expression data Bioinformatics 18 2002 39 50
    • (2002) Bioinformatics , vol.18 , pp. 39-50
    • Nguyen, D.V.1    Rocke, D.M.2
  • 56
    • 22944456563 scopus 로고    scopus 로고
    • Dimension reduction-based penalized logistic regression for cancer classification using microarray data
    • L. Shen, and E.C. Tan Dimension reduction-based penalized logistic regression for cancer classification using microarray data IEEE Transactions on Computational Biology and Bioinformatics 2 2005 166 175
    • (2005) IEEE Transactions on Computational Biology and Bioinformatics , vol.2 , pp. 166-175
    • Shen, L.1    Tan, E.C.2
  • 57
    • 0345327592 scopus 로고    scopus 로고
    • A simple and efficient algorithm for gene selection using sparse logistic regression
    • S.K. Shevade, and S.S. Keerthi A simple and efficient algorithm for gene selection using sparse logistic regression Bioinformatics 19 2003 2246 2253
    • (2003) Bioinformatics , vol.19 , pp. 2246-2253
    • Shevade, S.K.1    Keerthi, S.S.2
  • 61
    • 33751002948 scopus 로고    scopus 로고
    • A novel feature selection approach: Combining feature wrappers and filters
    • O. Uncu, and I.B. Türksen A novel feature selection approach: Combining feature wrappers and filters Information Sciences 177 2007 449 466
    • (2007) Information Sciences , vol.177 , pp. 449-466
    • Uncu, O.1    Türksen, I.B.2
  • 62
    • 0033257342 scopus 로고    scopus 로고
    • A genetic algorithm to select variables in logistic regression: Example in the domain of myocardial infarct
    • S. Vinterbo, and L. Ohno-Machado A genetic algorithm to select variables in logistic regression: Example in the domain of myocardial infarct Journal of the American Medical Informatics Association 6 1999 984 988
    • (1999) Journal of the American Medical Informatics Association , vol.6 , pp. 984-988
    • Vinterbo, S.1    Ohno-Machado, L.2
  • 65
  • 68
    • 15944363312 scopus 로고    scopus 로고
    • Classification of gene microarrays by penalized logistic regression
    • J. Zhu, and T. Hastie Classification of gene microarrays by penalized logistic regression Biostatistics 5 2004 427 443
    • (2004) Biostatistics , vol.5 , pp. 427-443
    • Zhu, J.1    Hastie, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.