{"type":"ScholarlyArticle","title":"Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification","authors":[{"name":"Teng Huang","slug":"teng-huang","given":"Teng","family":"Huang","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":"Bin-Bin Jia","slug":"bin-bin-jia","given":"Bin-Bin","family":"Jia","affiliation":"College of Electrical and Information Engineering , Lanzhou University of Technology , Lanzhou 730050 , China"},{"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":"In multi-dimensional classification (MDC), each training example is associated with multiple class variables from different class spaces. However, it is rather costly to collect labeled MDC examples which have to be annotated from several dimensions (class spaces). To reduce the labeling cost, we attempt to deal with the MDC problem under the semi-supervised learning setting. Accordingly, a novel MDC approach named PLAP is proposed to solve the resulting semi-supervised MDC problem. Overall, PLAP works under the label propagation framework to utilize unlabeled data. To further consider dependencies among class spaces, PLAP deals with each class space in a progressive manner, where the previous propagation results will be used to initialize the current propagation procedure and all processed class spaces and the current one will be regarded as an entirety. Experiments validate the effectiveness of the proposed approach.","doi":"10.24963/ijcai.2023/425","url":"https://ijcai.org/proceedings/2023/425","pdf":"https://ijcai.org/proceedings/2023/0425.pdf","markdown":"https://ijcai.org/proceedings/2023/425.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/425","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":425,"pages":{"first":3821,"last":3829},"keywords":["Machine Learning: ML: Classification","Machine Learning: ML: Multi-label","Machine Learning: ML: Semi-supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Arias et al., 2016] Jacinto Arias, José A. 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