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Instance Selection in Text Classification Using the Silhouette Coefficient Measure

  • Debangana Dey
  • Thamar Solorio
  • Manuel Montes y Gómez
  • Hugo Jair Escalante
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)

Abstract

The paper proposes the use of the Silhouette Coefficient (SC) as a ranking measure to perform instance selection in text classification. Our selection criterion was to keep instances with mid-range SC values while removing the instances with high and low SC values. We evaluated our hypothesis across three well-known datasets and various machine learning algorithms. The results show that our method helps to achieve the best trade-off between classification accuracy and training time.

Keywords

Instance Selection Outlier Elimination Text Classification Supervised Machine Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Debangana Dey
    • 1
  • Thamar Solorio
    • 1
  • Manuel Montes y Gómez
    • 1
    • 2
  • Hugo Jair Escalante
    • 2
    • 3
  1. 1.Department of Computer and Information SciencesUniversity of Alabama at BirminghamBirminghamUSA
  2. 2.National Institute of Astrophysics, Optics and ElectronicsPueblaMexico
  3. 3.Universidad Autonoma de Nuevo LeonSan Nicolas de los GarzaMexico

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