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Comparison of Local Classification Methods

  • Julia Schiffner
  • Claus Weihs
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

In this paper four local classification methods are described and their statistical properties in the case of local data generating processes (LDGPs) are compared. In order to systematically compare the local methods and LDA as global standard technique, they are applied to a variety of situations which are simulated by experimental design. This way, it is possible to identify characteristics of the data that influence the classification performances of individual methods. For the simulated data sets the local methods on the average yield lower error rates than LDA. Additionally, based on the estimated effects of the influencing factors, groups of similar methods are found and the differences between these groups are revealed. Furthermore, it is possible to recommend certain methods for special data structures.

Keywords

Linear Discriminant Anal Class Center Special Data Structure Hierarchical Mixture Class Conditional Density 
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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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Julia Schiffner
    • 1
  • Claus Weihs
    • 1
  1. 1.Department of StatisticsUniversity of DortmundDortmundGermany

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