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Feature Ranking Methods Used for Selection of Prototypes

  • Marcin Blachnik
  • Włodzisław Duch
  • Tomasz Maszczyk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

Prototype selection, as a preprocessing step in machine learning, is effective in decreasing the computational cost of classification task by reducing the number of retained instances. This goal is obtained by shrinking the level of noise and rejecting the irrelevant data. Prototypes may be also used to understand the data through improving comprehensibility of results. In the paper we discus an approach for instance selection based on techniques known from feature selection pointing out the dualism between feature and instance selection. Finally some experiments are shown which uses feature ranking methods for instance selection.

Keywords

Feature Selection Mutual Information Feature Ranking Instance Selection Ranking Criterion 
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 2012

Authors and Affiliations

  • Marcin Blachnik
    • 3
  • Włodzisław Duch
    • 1
    • 2
  • Tomasz Maszczyk
    • 1
  1. 1.Department of InformaticsNicolaus Copernicus UniversityToruńPoland
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingapore
  3. 3.Dept. of Management & InformaticsSilesian University of TechnologyKatowicePoland

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