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International Conference on Hybrid Artificial Intelligence Systems

HAIS 2016: Hybrid Artificial Intelligent Systems pp 487–499Cite as

R Ultimate Multilabel Dataset Repository

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Part of the book series: Lecture Notes in Computer Science ((LNAI,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.

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Notes

  1. 1.

    http://GitHub.com.

  2. 2.

    https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html.

  3. 3.

    http://sourceforge.net/projects/meka/files/Datasets/.

  4. 4.

    http://mulan.sourceforge.net/datasets-mlc.html.

  5. 5.

    http://sci2s.ugr.es/keel/multilabel.php.

  6. 6.

    This is a compressed file format of the representation of R objects in memory.

  7. 7.

    https://github.com/fcharte/mldr.datasets/blob/master/additional-data/README.md.

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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.

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain

    Francisco Charte, David Charte & Francisco Herrera

  2. Department of Computer Science, University of Jaén, Jaén, Spain

    Antonio Rivera & María José del Jesus

Authors
  1. Francisco Charte
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  2. David Charte
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  3. Antonio Rivera
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  4. María José del Jesus
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  5. Francisco Herrera
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Corresponding author

Correspondence to Francisco Charte .

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Editors and Affiliations

  1. Universidad Pablo de Olavide, Sevilla, Spain

    Francisco Martínez-Álvarez

  2. Universidad Pablo de Olavide, Sevilla, Spain

    Alicia Troncoso

  3. University of Salamanca, Salamanca, Spain

    Héctor Quintián

  4. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Charte, F., Charte, D., Rivera, A., del Jesus, M.J., Herrera, F. (2016). R Ultimate Multilabel Dataset Repository. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_41

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  • DOI: https://doi.org/10.1007/978-3-319-32034-2_41

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