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Benchmarking local classification methods

Author

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  • Bernd Bischl
  • Julia Schiffner
  • Claus Weihs

Abstract

In recent years in the fields of statistics and machine learning an increasing amount of so called local classification methods has been developed. Local approaches to classification are not new, but have lately become popular. Well-known examples are the $$k$$ nearest neighbors method and classification trees. However, in most publications on this topic the term “local” is used without further explanation of its particular meaning. Only little is known about the properties of local methods and the types of classification problems for which they may be beneficial. We explain the basic principles and introduce the most important variants of local methods. To our knowledge there are very few extensive studies in the literature that compare several types of local methods and global methods across many data sets. In order to assess their performance we conduct a benchmark study on real-world and synthetic tasks. We cluster data sets and considered learning algorithms with regard to the obtained performance structures and try to relate our theoretical considerations and intuitions to these results. We also address some general issues of benchmark studies and cover some pitfalls, extensions and improvements. Copyright Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Bernd Bischl & Julia Schiffner & Claus Weihs, 2013. "Benchmarking local classification methods," Computational Statistics, Springer, vol. 28(6), pages 2599-2619, December.
  • Handle: RePEc:spr:compst:v:28:y:2013:i:6:p:2599-2619
    DOI: 10.1007/s00180-013-0420-y
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    References listed on IDEAS

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    1. Hand D.J. & Vinciotti V., 2003. "Local Versus Global Models for Classification Problems: Fitting Models Where it Matters," The American Statistician, American Statistical Association, vol. 57, pages 124-131, May.
    2. Binder Harald & Schumacher Martin, 2008. "Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-28, March.
    3. Zhang, Chun-Xia & Zhang, Jiang-She, 2008. "A local boosting algorithm for solving classification problems," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1928-1941, January.
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