{"type":"ScholarlyArticle","title":"Label-Sensitive Task Grouping by Bayesian Nonparametric Approach for Multi-Task Multi-Label Learning","authors":[{"name":"Xiao Zhang","slug":"xiao-zhang","given":"Xiao","family":"Zhang","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"},{"name":"Wenzhong Li","slug":"wenzhong-li","given":"Wenzhong","family":"Li","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China; Uni- versity of Chinese Academy of Sciences , Beijing , 100049 , China."},{"name":"Vu Nguyen","slug":"vu-nguyen","given":"Vu","family":"Nguyen","affiliation":"Center for Pattern Recognition and Data Analytics , Deakin University , Australia"},{"name":"Fuzhen Zhuang","slug":"fuzhen-zhuang","given":"Fuzhen","family":"Zhuang","affiliation":"Key Lab of IIP of CAS , Institute of Computing Technology , CAS Beijing , China"},{"name":"Hui Xiong","slug":"hui-xiong","given":"Hui","family":"Xiong","affiliation":"Management Science & Information Systems , Rutgers University , USA"},{"name":"Sanglu Lu","slug":"sanglu-lu","given":"Sanglu","family":"Lu","affiliation":"State Key Laboratory for Novel Software Technology , Nanjing University , China"}],"abstract":"Multi-label learning is widely applied in many real-world applications, such as image and gene annotation. While most of the existing multi-label learning models focus on the single-task learning problem, there are always some tasks that share some commonalities, which can help each other to improve the learning performances if the knowledge in the similar tasks can be smartly shared. In this paper, we propose a LABel-sensitive TAsk Grouping framework, named LABTAG, based on Bayesian nonparametric approach for multi-task multi-label classification. The proposed framework explores the label correlations to capture feature-label patterns, and clusters similar tasks into groups with shared knowledge, which are learned jointly to produce a strengthened multi-task multi-label model. We evaluate the model performance on three public multi-task multi-label data sets, and the results show that LABTAG outperforms the compared baselines with a significant margin.","doi":"10.24963/ijcai.2018/434","url":"https://ijcai.org/proceedings/2018/434","pdf":"https://ijcai.org/proceedings/2018/0434.pdf","markdown":"https://ijcai.org/proceedings/2018/434.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/434","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":434,"pages":{"first":3125,"last":3131},"keywords":["Machine Learning: Transfer, Adaptation, Multi-task Learning","Machine Learning: Multi-instance;Multi-label;Multi-view learning"],"videos":[],"references":[{"position":1,"raw":"[Adomavicius and Tuzhilin, 2015] Gediminas Adomavicius and Alexander Tuzhilin. 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