{"type":"ScholarlyArticle","title":"Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning","authors":[{"name":"Wei Tang","slug":"wei-tang","given":"Wei","family":"Tang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Lab. of Computer Network and Information Integration ( Southeast University) , MoE , China"},{"name":"Weijia Zhang","slug":"weijia-zhang","given":"Weijia","family":"Zhang","affiliation":"School of Information and Physical Sciences , The University of Newcastle , NSW 2308 , Australia"},{"name":"Min-Ling Zhang","slug":"min-ling-zhang","given":"Min-Ling","family":"Zhang","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Lab. of Computer Network and Information Integration ( Southeast University) , MoE , China"}],"abstract":"Multi-instance partial-label learning (MIPL) addresses scenarios where each training sample is represented as a multi-instance bag associated with a candidate label set containing one true label and several false positives. Existing MIPL algorithms have primarily focused on mapping multi-instance bags to candidate label sets for disambiguation, disregarding the intrinsic properties of the label space and the supervised information provided by non-candidate label sets. In this paper, we propose an algorithm named ELIMIPL, i.e., Exploiting conjugate Label Information for Multi-Instance Partial-Label learning, which exploits the conjugate label information to improve the disambiguation performance. To achieve this, we extract the label information embedded in both candidate and non-candidate label sets, incorporating the intrinsic properties of the label space. Experimental results obtained from benchmark and real-world datasets demonstrate the superiority of the proposed ELIMIPL over existing MIPL algorithms and other well-established partial-label learning algorithms.","doi":"10.24963/ijcai.2024/550","url":"https://ijcai.org/proceedings/2024/550","pdf":"https://ijcai.org/proceedings/2024/0550.pdf","markdown":"https://ijcai.org/proceedings/2024/550.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/550","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":550,"pages":{"first":4973,"last":4981},"keywords":["Machine Learning: ML: Weakly supervised learning","Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Amores, 2013] Jaume Amores. 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