{"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/lnemlc-label-network-embeddings-for-multi","title":"LNEMLC: Label Network Embeddings for Multi-Label Classification","arxiv_id":"1812.02956","date":"2018-12-07","proceeding":null,"authors":["Piotr Szymański","Tomasz Kajdanowicz","Nitesh Chawla"],"abstract":"Multi-label classification aims to classify instances with discrete\nnon-exclusive labels. Most approaches on multi-label classification focus on\neffective adaptation or transformation of existing binary and multi-class\nlearning approaches but fail in modelling the joint probability of labels or do\nnot preserve generalization abilities for unseen label combinations. To address\nthese issues we propose a new multi-label classification scheme, LNEMLC - Label\nNetwork Embedding for Multi-Label Classification, that embeds the label network\nand uses it to extend input space in learning and inference of any base\nmulti-label classifier. The approach allows capturing of labels' joint\nprobability at low computational complexity providing results comparable to the\nbest methods reported in the literature. We demonstrate how the method reveals\nstatistically significant improvements over the simple kNN baseline classifier.\nWe also provide hints for selecting the robust configuration that works\nsatisfactorily across data domains.","url_abs":"http://arxiv.org/abs/1812.02956v2","url_pdf":"http://arxiv.org/pdf/1812.02956v2.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":"lnemlc-label-network-embeddings-for-multi","repo_url":"https://github.com/evantkchong/LEPAR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"network-embedding","task_name":"Network Embedding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}