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An Empirical Analysis of Under-Sampling Techniques to Balance a Protein Structural Class Dataset

  • Marcilio C. P. de Souto
  • Valnaide G. Bittencourt
  • Jose A. F. Costa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

There have been a great deal of research on learning from imbalanced datasets. Among the widely used methods proposed to solve such a problem, the most common are based either on under or over sampling of the original dataset. In this work, we evaluate several methods of under-sampling, such as Tomek Links, with the goal of improving the performance of the classifiers generated by different ML algorithms (decision trees, support vector machines, among others) applied to problem of determining the structural similarity of proteins.

Keywords

Support Vector Machine Majority Class Minority Class Neighbor Rule Imbalanced Dataset 
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 2006

Authors and Affiliations

  • Marcilio C. P. de Souto
    • 1
  • Valnaide G. Bittencourt
    • 2
  • Jose A. F. Costa
    • 3
  1. 1.Department of Informatics and Applied MathematicsFederal University of Rio Grande do NorteNatal-RNBrazil
  2. 2.Department of Computing and AutomationFederal University of Rio Grande do NorteNatal-RNBrazil
  3. 3.Department of Electric EngineeringFederal University of Rio Grande do NorteNatal-RNBrazil

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