{"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-reference-pair-encoding-for","title":"Cost-Sensitive Reference Pair Encoding for Multi-Label Learning","arxiv_id":"1611.09461","date":"2016-11-29","proceeding":null,"authors":["Yao-Yuan Yang","Kuan-Hao Huang","Chih-Wei Chang","Hsuan-Tien Lin"],"abstract":"Label space expansion for multi-label classification (MLC) is a methodology\nthat encodes the original label vectors to higher dimensional codes before\ntraining and decodes the predicted codes back to the label vectors during\ntesting. The methodology has been demonstrated to improve the performance of\nMLC algorithms when coupled with off-the-shelf error-correcting codes for\nencoding and decoding. Nevertheless, such a coding scheme can be complicated to\nimplement, and cannot easily satisfy a common application need of\ncost-sensitive MLC---adapting to different evaluation criteria of interest. In\nthis work, we show that a simpler coding scheme based on the concept of a\nreference pair of label vectors achieves cost-sensitivity more naturally. In\nparticular, our proposed cost-sensitive reference pair encoding (CSRPE)\nalgorithm contains cluster-based encoding, weight-based training and\nvoting-based decoding steps, all utilizing the cost information. Furthermore,\nwe leverage the cost information embedded in the code space of CSRPE to propose\na novel active learning algorithm for cost-sensitive MLC. Extensive\nexperimental results verify that CSRPE performs better than state-of-the-art\nalgorithms across different MLC criteria. The results also demonstrate that the\nCSRPE-backed active learning algorithm is superior to existing algorithms for\nactive MLC, and further justify the usefulness of CSRPE.","url_abs":"http://arxiv.org/abs/1611.09461v3","url_pdf":"http://arxiv.org/pdf/1611.09461v3.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-reference-pair-encoding-for","repo_url":"https://github.com/yangarbiter/multilabel-learn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"},{"task_slug":"multi-label-classification-2","task_name":"MUlTI-LABEL-ClASSIFICATION"},{"task_slug":"multi-label-classification","task_name":"Multi-Label Classification"},{"task_slug":"multi-label-learning","task_name":"Multi-Label Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.09461","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}