{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:49:10Z","timestamp":1747216150297,"version":"3.40.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643685489"}],"license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,16]]},"abstract":"<jats:p>In partial label learning, each training sample corresponds to a set of candidate labels. The ground-truth label, hidden within this set, cannot be directly obtained during the training phase. The key to solving the partial label learning problem is to obtain ground-truth labels through label disambiguation. Existing works often rely on the label averaging assumption and do not fully investigate the class imbalance. Tail ground-truth labels are often overwhelmed by head pseudo-labels. The incorrectly identified labels could have contagiously negative impacts on the final predictions. In this paper, we propose a cost-guided retraining strategy, which achieves guidance and correction of disambiguation results, and provides instance-based class imbalance concerns for candidate labels. This approach significantly enhances the algorithm\u2019s ability to handle class imbalance problems. The superiority of our method is demonstrated using 8 real-world datasets and 5 evaluation metrics. Code is available at https:\/\/github.com\/DerrickZzyR\/PL-CGR<\/jats:p>","DOI":"10.3233\/faia240737","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:16:41Z","timestamp":1729171001000},"source":"Crossref","is-referenced-by-count":0,"title":["Partial Label Learning via Cost-Guided Retraining"],"prefix":"10.3233","author":[{"given":"Zhaoyuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Computer and Information Security, Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenbing","family":"Liu","sequence":"additional","affiliation":[{"name":"Computer and Information Security, Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2284-5154","authenticated-orcid":false,"given":"Haoxiang","family":"Lu","sequence":"additional","affiliation":[{"name":"Computer and Information Security, Guilin University of Electronic Technology, Guilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2024"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA240737","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T13:16:41Z","timestamp":1729171001000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA240737"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,16]]},"ISBN":["9781643685489"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia240737","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"type":"print","value":"0922-6389"},{"type":"electronic","value":"1879-8314"}],"subject":[],"published":{"date-parts":[[2024,10,16]]}}}