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Bagging Combined Classifiers

  • Torsten Hothorn
  • Berthold Lausen
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Aggregated classifiers have proven to be successful in reducing misclas­sification error in a wide range of classification problems. One of the most popular is bagging. But often simple procedures perform comparably in specific applications. For example, linear discriminant analysis (LDA) provides efficient classifiers if the underlying class structure is linear regarding the predictors.

We suggest bagging for a combination of tree classifiers and LDA. The out-of-bag sample is used as an independent learning sample for the computation of linear discriminant functions. The corresponding discriminant variables of the bootstrap sample are used as additional predictors for a classification tree. We illustrate the proposal by a glaucoma classification with laser scanning image data. Moreover, we analyse the properties with a simulation study and benchmark data sets. In summary, our proposal has misclassification error comparable to LDA when LDA performs best and comparable to bagged trees when bagged trees perform best.

Keywords

Linear Discriminant Analysis Bootstrap Sample Classification Tree Optic Nerve Head Misclassification Error 
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-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Torsten Hothorn
    • 1
  • Berthold Lausen
    • 1
  1. 1.Department of Medical Informatics, Biometry and EpidemiologyUniversity Erlangen-NurembergErlangenGermany

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