R Ultimate Multilabel Dataset Repository

Comunicación en congreso 2016

Autores: Francisco Charte Ojeda, David Charte, Antonio Jesús Rivera Rivas, Maria José del Jesus Díaz, Francisco Herrera Triguero

Páginas 487-499

Cómo citar esta publicación

Charte Ojeda, F., Charte, D. ", Rivera Rivas, A. J., del Jesus Díaz, M. J., & Herrera Triguero, F. (2016). R Ultimate Multilabel Dataset Repository. 487-499. Seville (Spain). https://doi.org/10.1007/978-3-319-32034-2_41
BibTeX

Resumen

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.

Financiación

Detalles

Date Published
4
Lugar del congreso
Seville (Spain)
ISBN
978-3-319-32033-5