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Ensemble Methods of Computational Inference

  • T. Hothorn
  • M. Dettling
  • P. Bühlmann
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Prognostic modeling of tumor classes, disease status, and survival time based on information obtained from gene expression profiling techniques is studied in this chapter. The basic principles of ensemble methods like bagging, random forests, and boosting are explained. The application of those methods to data from patients suffering acute lymphoblastic leukemia or renal cell cancer is illustrated. The problem of identifying the best method for a certain prediction task is addressed by means of benchmark experiments.

Keywords

Acute Lymphoblastic Leukemia Random Forest Bootstrap Sample Renal Cell Cancer Ensemble Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  • T. Hothorn
  • M. Dettling
  • P. Bühlmann

There are no affiliations available

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