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A Transformation Approach Towards Big Data Multilabel Decision Trees

  • Antonio Jesús Rivera RivasEmail author
  • Francisco Charte Ojeda
  • Francisco Javier Pulgar
  • Maria Jose del Jesus
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10305)

Abstract

A large amount of the data processed nowadays is multilabel in nature. This means that every pattern usually belongs to several categories at once. Multilabel data are abundant, and most multilabel datasets are quite large. This causes that many multilabel classification methods struggle with their processing. Tackling this task by means of big data methods seems a logical choice. However, this approach has been scarcely explored by now. The present work introduces several big data multilabel classifiers, all of them based on decision trees. After detailing how they have been designed, their predictive performance, as well as the execution time, are analyzed.

Keywords

Multilabel classification Big data Decision trees 

Notes

Acknowledgments

This work is partially supported by the Spanish Ministry of Science and Technology under project TIN2015-68454-R.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Antonio Jesús Rivera Rivas
    • 1
    Email author
  • Francisco Charte Ojeda
    • 1
  • Francisco Javier Pulgar
    • 1
  • Maria Jose del Jesus
    • 1
  1. 1.Department of Computer ScienceUniversity of JaénJaénSpain

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