{"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/cost-sensitive-label-embedding-for-multi","title":"Cost-Sensitive Label Embedding for Multi-Label Classification","arxiv_id":"1603.09048","date":"2016-03-30","proceeding":null,"authors":["Kuan-Hao Huang","Hsuan-Tien Lin"],"abstract":"Label embedding (LE) is an important family of multi-label classification\nalgorithms that digest the label information jointly for better performance.\nDifferent real-world applications evaluate performance by different cost\nfunctions of interest. Current LE algorithms often aim to optimize one specific\ncost function, but they can suffer from bad performance with respect to other\ncost functions. In this paper, we resolve the performance issue by proposing a\nnovel cost-sensitive LE algorithm that takes the cost function of interest into\naccount. The proposed algorithm, cost-sensitive label embedding with\nmultidimensional scaling (CLEMS), approximates the cost information with the\ndistances of the embedded vectors by using the classic multidimensional scaling\napproach for manifold learning. CLEMS is able to deal with both symmetric and\nasymmetric cost functions, and effectively makes cost-sensitive decisions by\nnearest-neighbor decoding within the embedded vectors. We derive theoretical\nresults that justify how CLEMS achieves the desired cost-sensitivity.\nFurthermore, extensive experimental results demonstrate that CLEMS is\nsignificantly better than a wide spectrum of existing LE algorithms and\nstate-of-the-art cost-sensitive algorithms across different cost functions.","url_abs":"http://arxiv.org/abs/1603.09048v5","url_pdf":"http://arxiv.org/pdf/1603.09048v5.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":"cost-sensitive-label-embedding-for-multi","repo_url":"https://github.com/ej0cl6/csmlc","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"cost-sensitive-label-embedding-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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}