Relevance and Redundancy Analysis for Ensemble Classifiers

  • Rakkrit Duangsoithong
  • Terry Windeatt
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5632)

Abstract

In machine learning systems, especially in medical applications, clinical datasets usually contain high dimensional feature spaces with relatively few samples that lead to poor classifier performance. To overcome this problem, feature selection and ensemble classification are applied in order to improve accuracy and stability. This research presents an analysis of the effect of removing irrelevant and redundant features with ensemble classifiers using five datasets and compared with floating search method. Eliminating redundant features provides better accuracy and computational time than removing irrelevant features of the ensemble.

Keywords

Feature selection Ensemble classification Redundant feature Irrelevant feature 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rakkrit Duangsoithong
    • 1
  • Terry Windeatt
    • 1
  1. 1.Center for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUnited Kingdom

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