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Identifying representative trees from ensembles
dc.contributor.authorBanerjee, Mousumien_US
dc.contributor.authorDing, Yingen_US
dc.contributor.authorNoone, Anne-Michelleen_US
dc.date.accessioned2012-07-12T17:24:30Z
dc.date.available2013-09-03T15:38:27Zen_US
dc.date.issued2012-07-10en_US
dc.identifier.citationBanerjee, Mousumi; Ding, Ying; Noone, Anne-Michelle (2012). "Identifying representative trees from ensembles." Statistics in Medicine 31(15): 1601-1616. <http://hdl.handle.net/2027.42/92082>en_US
dc.identifier.issn0277-6715en_US
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/92082
dc.publisherJohn Wiley & Sons, Ltden_US
dc.subject.otherRandom Foresten_US
dc.subject.otherBaggingen_US
dc.subject.otherTree Similarity Metricen_US
dc.subject.otherRepresentative Treesen_US
dc.subject.otherOut‐Of‐Bag Erroren_US
dc.titleIdentifying representative trees from ensemblesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelPublic Healthen_US
dc.subject.hlbsecondlevelMedicine (General)en_US
dc.subject.hlbtoplevelScienceen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.peerreviewedPeer Revieweden_US
dc.identifier.pmid22302520en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/92082/1/sim4492.pdf
dc.identifier.doi10.1002/sim.4492en_US
dc.identifier.sourceStatistics in Medicineen_US
dc.identifier.citedreferenceIshwaran H, Kogalur UB. Random survival forests for R. Rnews 2007; 7 ( 2 ): 25 – 31.en_US
dc.identifier.citedreferenceFreund Y, Schapire RE. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, Bari, Italy, 1996; 148 – 156.en_US
dc.identifier.citedreferenceChipman HA, George EI, McCulloch RE. Managing multiple models. In Artificial Intelligence and Statistics, Jaakkola T, Richardson T (eds). Morgan Kaufmann, 2001.en_US
dc.identifier.citedreferenceShannon WD, Banks D. Combining classification trees using MLE. Statistics in Medicine 1999; 18: 727 – 740.en_US
dc.identifier.citedreferenceHarrell FE, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. The Journal of the American Medical Association 1982; 18: 2543 – 2546.en_US
dc.identifier.citedreferenceFreedman GM, Hanlon AL, Fowble BL, Anderson PR, Nicoloau N. Recursive partitioning identifies patients at high and low risk for ipsilateral tumor recurrence after breast‐conserving surgery and radiation. Journal of Clinical Oncology 2002; 20: 4015 – 4021.en_US
dc.identifier.citedreferenceBanerjee M, George J, Song EY, Roy A, Hryniuk W. Tree‐based model for breast cancer prognostication. Journal of Clinical Oncology 2004; 22: 2567 – 2575.en_US
dc.identifier.citedreferenceSegal M, Barbour JD, Grant RM. Relating HIV‐1 sequence variation to replication capacity via trees and forests. Statistical Applications in Genetics and Molecular Biology 2004; 3 ( Iss. 1 ). Article 2.en_US
dc.identifier.citedreferenceBreiman L. Bagging predictors. Machine Learning 1996; 24: 123 – 140.en_US
dc.identifier.citedreferenceBreiman L. Random forests. Machine Learning 2001; 45: 5 – 32.en_US
dc.identifier.citedreferenceIshwaran H, Blackstone EH, Pothier CE, Lauer MS. Relative risk forests for exercise heart rate recovery as a predictor of mortality. Journal of the American Statistical Association 2004; 99: 591 – 600.en_US
dc.identifier.citedreferenceQuinlan J. Bagging, boosting, and C4.5. In Proceedings Thirteenth American Association for Artificial Intelligence National Conference on Artificial Intelligence. CA. AAAI Press: Menlo Park, 1996; 725 – 730.en_US
dc.identifier.citedreferenceGraf E, Schmoor C, Sauerbrei W, Schumacher M. Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine 1999; 18: 2529 – 2545.en_US
dc.identifier.citedreferenceTherneau TM, Atkinson B. RPART: recursive partitioning, 2007. R port by Brian Ripley < ripley@stats.ox.ac.uk >. R package version 3.1‐36.en_US
dc.identifier.citedreferenceTherneau TM, Atkinson B. An introduction to recursive partitioning using the RPART routines. Technical report, Mayo Foundationm, 1997.en_US
dc.identifier.citedreferenceMay M, Royston P, Egger M, Justice AC, Sterne J. Development and validation of a prognostic model for survival time data: application to prognosis of HIV positive patients treated with antiretroviral therapy. Statistics in Medicine 2004; 23: 2375 – 2398.en_US
dc.identifier.citedreferenceBreiman L, Friedman JH, Olshen RA, Stone CJ. Classification and Regression Trees. Wadsworth: Belmont, California, 1984.en_US
dc.identifier.citedreferenceZhang H, Singer B. Recursive Partitioning in the Health Sciences. Springer: New York, 1999.en_US
dc.identifier.citedreferenceHastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning. Springer: New York, 2001.en_US
dc.identifier.citedreferenceCutler A, Cutler DR, Stevens JR. Tree‐based methods. In High‐Dimensional Data Analysis in Cancer Research, Li X, Xu R (eds). Springer, Inc: New York, 2009; 83 – 101.en_US
dc.identifier.citedreferenceLeBlanc M, Crowley J. Relative risk trees for censored survival data. Biometrics 1992; 48: 411 – 425.en_US
dc.identifier.citedreferenceLeBlanc M. Tree‐based methods for prognostic stratification. In Handbook of Statistics in Clinical Oncology, Crowley J (ed.). Marcel Dekker, Inc: New York, 2001; 457 – 472.en_US
dc.identifier.citedreferenceKeles S, Segal MR. Residual‐based tree‐structured survival analysis. Statistics in Medicine 2002; 21: 313 – 326.en_US
dc.identifier.citedreferenceBanerjee M, Noone A. Tree‐based methods for survival data. In Statistical Advances in the Biomedical Sciences, Biswas A, Datta S, Fine JP, Segal MR (eds). John Wiley & Sons: New Jersey, 2008; 265 – 285.en_US
dc.identifier.citedreferenceZhang H, Yu CY, Singer B, Xiong MM. Recursive partitioning for tumor classification with gene expression microarray data. Proceedings of the National Academy of Sciences 2001; 98: 6730 – 6735.en_US
dc.identifier.citedreferenceBanerjee M, Biswas D, Sakr W, Wood DP Jr. Recursive partitioning for prognostic grouping of patients with clinically localized prostate carcinoma. Cancer 2000; 89: 404 – 411.en_US
dc.identifier.citedreferenceKatz A, Buchholz TA, Thames H, Smith CD, McNeese MD, Theriault R, Singletary SE, Strom EA. Recursive partitioning analysis of locoregional recurrence patterns following mastectomy: implications for adjuvant irradiation. International Journal of Radiation Oncology, Biology, Physics 2001; 50: 397 – 403.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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