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R Ultimate Multilabel Dataset Repository

  • Francisco Charte
  • David Charte
  • Antonio Rivera
  • María José del Jesus
  • Francisco Herrera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

Abstract

Multilabeled data is everywhere on the Internet. From news on digital media and entries published in blogs, to videos hosted in Youtube, every object is usually tagged with a set of labels. This way they can be categorized into several non-exclusive groups. However, publicly available multilabel datasets (MLDs) are not so common. There is a handful of websites providing a few of them, using disparate file formats. Finding proper MLDs, converting them into the correct format and locating the appropriate bibliographic data to cite them are some of the difficulties usually confronted by researchers and practitioners.

In this paper RUMDR (R Ultimate Multilabel Dataset Repository), a new multilabel dataset repository aimed to fuse all public MLDs, is introduced, along with mldr.datasets, an R package which eases the process of retrieving MLDs and their bibliographic information, exporting them to the desired file formats and partitioning them.

Keywords

Multilabel Datasets Software 

Notes

Acknowledgments

This work was partially supported by the Spanish Ministry of Science and Technology under projects TIN2014-57251-P and TIN2012-33856, and the Andalusian regional projects P10-TIC-06858 and P11-TIC-7765.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francisco Charte
    • 1
  • David Charte
    • 1
  • Antonio Rivera
    • 2
  • María José del Jesus
    • 2
  • Francisco Herrera
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversity of JaénJaénSpain

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