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Effect of Feature Selection on Bagging Classifiers Based on Kernel Density Estimators

  • Edgar Acuña
  • Alex Rojas
  • Frida Coaquira
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

A combination of classification rules (classifiers) is known as an Ensemble, and in general it is more accurate than the individual classifiers used to build it. One method to construct an Ensemble is Bagging introduced by Breiman, (1996). This method relies on resampling techniques to obtain different training sets for each of the classifiers. Previous work has shown that Bagging is very effective for unstable classifiers. In this paper we present some results in application of Bagging to classifiers where the class conditional density is estimated using kernel density estimators. The effect of feature selection in bagging is also considered.

Keywords

Feature Selection Linear Discriminant Analysis Kernel Density Misclassification Error Kernel Density Estimator 
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

  • Edgar Acuña
    • 1
  • Alex Rojas
    • 2
  • Frida Coaquira
    • 1
  1. 1.Department of MathematicsUniversity of Puerto Rico at MayaguezMayaguezPuerto Rico
  2. 2.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA

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