{"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/multi-label-methods-for-prediction-with","title":"Multi-label Methods for Prediction with Sequential Data","arxiv_id":"1609.08349","date":"2016-09-27","proceeding":null,"authors":["Jesse Read","Luca Martino","Jaakko Hollmén"],"abstract":"The number of methods available for classification of multi-label data has\nincreased rapidly over recent years, yet relatively few links have been made\nwith the related task of classification of sequential data. If labels indices\nare considered as time indices, the problems can often be seen as equivalent.\nIn this paper we detect and elaborate on connections between multi-label\nmethods and Markovian models, and study the suitability of multi-label methods\nfor prediction in sequential data. From this study we draw upon the most\nsuitable techniques from the area and develop two novel competitive approaches\nwhich can be applied to either kind of data. We carry out an empirical\nevaluation investigating performance on real-world sequential-prediction tasks:\nelectricity demand, and route prediction. As well as showing that several\npopular multi-label algorithms are in fact easily applicable to sequencing\ntasks, our novel approaches, which benefit from a unified view of these areas,\nprove very competitive against established methods.","url_abs":"http://arxiv.org/abs/1609.08349v2","url_pdf":"http://arxiv.org/pdf/1609.08349v2.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":"multi-label-methods-for-prediction-with","repo_url":"https://github.com/abimur-123/Canvass_codingchallenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"prediction","task_name":"Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1609.08349","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}