{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tips-guidelines-and-tools-for-managing-multi","title":"Tips, guidelines and tools for managing multi-label datasets: the mldr.datasets R package and the Cometa data repository","arxiv_id":"1802.03568","date":"2018-02-10","proceeding":null,"authors":["Francisco Charte","Antonio J. Rivera","David Charte","María J. del Jesus","Francisco Herrera"],"abstract":"New proposals in the field of multi-label learning algorithms have been\ngrowing in number steadily over the last few years. The experimentation\nassociated with each of them always goes through the same phases: selection of\ndatasets, partitioning, training, analysis of results and, finally, comparison\nwith existing methods. This last step is often hampered since it involves using\nexactly the same datasets, partitioned in the same way and using the same\nvalidation strategy. In this paper we present a set of tools whose objective is\nto facilitate the management of multi-label datasets, aiming to standardize the\nexperimentation procedure. The two main tools are an R package, mldr.datasets,\nand a web repository with datasets, Cometa. Together, these tools will simplify\nthe collection of datasets, their partitioning, documentation and export to\nmultiple formats, among other functions. Some tips, recommendations and\nguidelines for a good experimental analysis of multi-label methods are also\npresented.","url_abs":"http://arxiv.org/abs/1802.03568v1","url_pdf":"http://arxiv.org/pdf/1802.03568v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tips-guidelines-and-tools-for-managing-multi","repo_url":"https://github.com/fdavidcl/cometa","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"},{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}