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Screening a Case Base for Stroke Disease Detection Read chapter Read first chapter

2016 | OriginalPaper | Chapter

R Ultimate Multilabel Dataset Repository

Authors : Francisco Charte, David Charte, Antonio Rivera, María José del Jesus, Francisco Herrera

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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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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previous chapter A Clustering-Based Method for Team Formation in Learning Environments
next chapter On the Impact of Dataset Complexity and Sampling Strategy in Multilabel Classifiers Performance
Footnotes
6
This is a compressed file format of the representation of R objects in memory.
 
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Metadata
Title
R Ultimate Multilabel Dataset Repository
Authors
Francisco Charte
David Charte
Antonio Rivera
María José del Jesus
Francisco Herrera
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

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

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