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Analyzing the Impact of Unbalanced Data on Web Spam Classification

  • J. Fdez-Glez
  • D. Ruano-Ordás
  • F. Fdez-Riverola
  • J. R. Méndez
  • R. Pavón
  • R. Laza
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 373)

Abstract

Web spam is a serious problem which nowadays continues to threaten search engines because the quality of their results can be severely degraded by the presence of illegitimate pages. With the aim of fighting against web spam, several works have been carried out trying to reduce the impact of spam content. Regardless of the type of developed approaches, all the proposals have been faced with the difficulty of dealing with a corpus in which the difference between the amount of legitimate pages and the number of web sites with spam content is extremely high. Unbalanced data is a well-known common problem present in many practical applications of machine learning, having significant effects on the performance of standard classifiers. Focusing on web spam detection, the objective of this work is two-fold: to evaluate the effect of the class imbalance ratio over popular classifiers such as Naïve Bayes, SVM and C5.0, and to assess how their performance can be improved when different types of techniques are combined in an unbalanced scenario.

Keywords

web spam detection unbalanced data sampling techniques ensemble of classifiers 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • J. Fdez-Glez
    • 1
  • D. Ruano-Ordás
    • 1
  • F. Fdez-Riverola
    • 1
  • J. R. Méndez
    • 1
  • R. Pavón
    • 1
  • R. Laza
    • 1
  1. 1.Dept. InformáticaUniversity of Vigo, Escuela Superior de Ingeniería Informática, Edificio PolitécnicoOurenseSpain

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