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Ensemble classification of paired data

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  • Adler, Werner
  • Brenning, Alexander
  • Potapov, Sergej
  • Schmid, Matthias
  • Lausen, Berthold
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    Abstract

    In many medical applications, data are taken from paired organs or from repeated measurements of the same organ or subject. Subject based as opposed to observation based evaluation of these data results in increased efficiency of the estimation of the misclassification rate. A subject based approach for classification in the generation of bootstrap samples of bagging and bundling methods is analyzed. A simulation model is used to compare the performance of different strategies to create the bootstrap samples which are used to grow individual trees. The proposed approach is compared to linear discriminant analysis, logistic regression, random forests and gradient boosting. Finally, the simulation results are applied to glaucoma diagnosis using both eyes of glaucoma patients and healthy controls. It is demonstrated that the proposed subject based resampling reduces the misclassification rate.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 5 (May)
    Pages: 1933-1941

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:5:p:1933-1941

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    Web page: http://www.elsevier.com/locate/csda

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    Keywords: Ensemble classification Glaucoma diagnosis Paired data;

    References

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    1. De Bock, Koen W. & Coussement, Kristof & Van den Poel, Dirk, 2010. "Ensemble classification based on generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1535-1546, June.
    2. Hothorn, Torsten & Lausen, Berthold, 2005. "Bundling classifiers by bagging trees," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1068-1078, June.
    3. Rokach, Lior, 2009. "Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4046-4072, October.
    4. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, January.
    5. Iranpanah, N. & Mohammadzadeh, M. & Taylor, C.C., 2011. "A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 578-587, January.
    6. Zhang, Chun-Xia & Zhang, Jiang-She & Zhang, Gai-Ying, 2009. "Using Boosting to prune Double-Bagging ensembles," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1218-1231, February.
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    Cited by:
    1. Werner Adler & Sergej Potapov & Berthold Lausen, 2011. "Classification of repeated measurements data using tree-based ensemble methods," Computational Statistics, Springer, vol. 26(2), pages 355-369, June.

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