A Study on the Noise Label Influence in Boosting Algorithms: AdaBoost, GBM and XGBoost

  • Anabel Gómez-Ríos
  • Julián Luengo
  • Francisco Herrera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10334)


In classification, class noise alludes to incorrect labelling of instances and it causes the classifiers to perform worse. In this contribution, we test the resistance against noise of the most influential boosting algorithms. We explain the fundamentals of these state-of-the-art algorithms, providing an unified notation to facilitate their comparison. We analyse how they carry out the classification, what loss functions use and what techniques employ under the boosting scheme.


Class noise Boosting Classification 



This work was supported by the National Research Project TIN2014-57251-P and Andalusian Research Plan P11-TIC-7765.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Anabel Gómez-Ríos
    • 1
  • Julián Luengo
    • 1
  • Francisco Herrera
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of Granada, CITIC-UGRGranadaSpain

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