R Ultimate Multilabel Dataset Repository

被引:15
作者
Charte, Francisco [1 ]
Charte, David [1 ]
Rivera, Antonio [2 ]
Jose del Jesus, Maria [2 ]
Herrera, Francisco [1 ]
机构
[1] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain
[2] Univ Jaen, Dept Comp Sci, Jaen, Spain
来源
HYBRID ARTIFICIAL INTELLIGENT SYSTEMS | 2016年 / 9648卷
关键词
Multilabel; Datasets; R; Software; CLASSIFICATION;
D O I
10.1007/978-3-319-32034-2_41
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:487 / 499
页数:13
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