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Support Vector Machine Failure in Imbalanced Datasets

  • I. A. IllanEmail author
  • J. M. Gorriz
  • J. Ramirez
  • F. J. Martinez-Murcia
  • D. Castillo-Barnes
  • F. Segovia
  • D. Salas-Gonzalez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)

Abstract

Imbalanced datasets often pose challenges in classification problems. In this work we study and quantify the problem of imbalanced classification using support vector machines (SVM). We identify the conditions under which a SVM failure occur, both theoretically and experimentally, and show that it can be relevant even in cases of very weakly imbalanced data. The guidelines for exploratory data analysis are presented to avoid the SVM failure.

Keywords

Support vector machines Imbalanced data SVM Data analysis SVM failure 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • I. A. Illan
    • 1
    Email author
  • J. M. Gorriz
    • 1
  • J. Ramirez
    • 1
  • F. J. Martinez-Murcia
    • 1
  • D. Castillo-Barnes
    • 1
  • F. Segovia
    • 1
  • D. Salas-Gonzalez
    • 1
  1. 1.Departamento de Teoria de la señal y ComunicacionesUniversidad de GranadaGranadaSpain

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