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Cascading an Emerging Pattern Based Classifier

  • Milton García-Borroto
  • José Fco. Martínez-Trinidad
  • Jesús Ariel Carrasco-Ochoa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6256)

Abstract

Emerging Pattern classifiers are accurate and easy to understand classifiers. However, they have two characteristics that can degrade their accuracy: global discretization of numerical attributes and high sensitivity to the support threshold value. In this paper, we introduce a novel algorithm to find emerging patterns without global discretization. Additionally, we propose a new method for building cascades of emerging pattern classifiers, which combines the higher accuracy of classifying with higher support thresholds with the lower levels of abstention of classifying with lower thresholds. Experimental results show that our cascade attains higher accuracy than other state-of-the-art classifiers, including one of the most accurate emerging pattern based classifier.

Keywords

Classifier Cascades Understandable classifiers Emerging pattern classifiers 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Milton García-Borroto
    • 1
    • 2
  • José Fco. Martínez-Trinidad
    • 2
  • Jesús Ariel Carrasco-Ochoa
    • 2
  1. 1.Centro de BioplantasCiego de AvilaCuba
  2. 2.Instituto Nacional de AstrofísicaÓptica y ElectrónicaSta. María TonanzintlaMéxico

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