@article {782,
	title = {A Comprehensive and Didactic Review on Multilabel Learning Software Tools},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	note = {TIN2015-68854-R},
	month = {03/2020},
	pages = {50330-50354},
	abstract = {Machine learning has become an everyday tool in so many fields that there is plenty of software to run many of these algorithms in every device, from supercomputers to embedded appliances. Most of these methods fall into the category known as standard learning, being supervised models (guided by pre-labeled examples) aimed to classify new patterns into exactly one category. This way, machine learning is in charge of getting rid of junk emails, labeling people in a picture, or detecting a fraudulent transaction when using a credit card. Aside from unsupervised learning methods, which are usually applied to group similar patterns, infer association rules and similar tasks, some non-standard supervised machine learning problems have been faced in late years. Among them, multilabel learning is arguably the most popular one. These algorithms aim to produce models in which each data pattern may be linked to several categories at once. Thus, a multilabel classifier generates a set of outputs instead of only one as a standard classifier does. However, software tools for multilabel learning tend to be scarce. This paper provides multilabel researchers with a comprehensive review of the currently available multilabel learning software. It is written following a didactic approach, focusing on how to accomplish each task rather than simply offering a list of programs and websites. The goal is to help finding the most appropriate resource to complete every step, from locating datasets and partitioning them to running many of the multilabel algorithms proposed in the literature until now.},
	keywords = {machine learning, multilabel, non-standard learning, software, Tools},
	doi = {https://doi.org/10.1109/ACCESS.2020.2979787},
	author = {Francisco Charte}
}
