The R Journal: article published in 2015, volume 7:2

Working with Multilabel Datasets in R: The mldr Package
Francisco Charte and David Charte , The R Journal (2015) 7:2, pages 149-162.

Abstract Most classification algorithms deal with datasets which have a set of input features, the variables to be used as predictors, and only one output class, the variable to be predicted. However, in late years many scenarios in which the classifier has to work with several outputs have come to life. Automatic labeling of text documents, image annotation or protein classification are among them. Multilabel datasets are the product of these new needs, and they have many specific traits. The mldr package allows the user to load datasets of this kind, obtain their characteristics, produce specialized plots, and manipulate them. The goal is to provide the exploratory tools needed to analyze multilabel datasets, as well as the transformation and manipulation functions that will make possible to apply binary and multiclass classification models to this data or the development of new multilabel classifiers. Thanks to its integrated user interface, the exploratory functions will be available even to non-specialized R users.

Received: 2015-03-09; online 2015-09-16
CRAN packages: RWeka, mldr, shiny, Rcmdr, rattle, XML, circlize, devtools, pROC, shiny
CRAN Task Views implied by cited CRAN packages: WebTechnologies, MachineLearning, Finance, NaturalLanguageProcessing

This article is licensed under a Creative Commons Attribution 3.0 Unported license .

@article{RJ-2015-027,
  author = {Francisco Charte and David Charte},
  title = {{Working with Multilabel Datasets in R: The mldr Package}},
  year = {2015},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2015-027},
  url = {https://doi.org/10.32614/RJ-2015-027},
  pages = {149--162},
  volume = {7},
  number = {2}
}