{"type":"ScholarlyArticle","title":"Towards Enabling Binary Decomposition for Partial Label Learning","authors":[{"name":"Xuan Wu","slug":"xuan-wu","given":"Xuan","family":"Wu","affiliation":"School of Computer Science and Engineering , Southeast University , Nanjing 210096 , China; Key Laboratory of Computer Network and Information Integration ( Southeast University) , MOE , 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 Laboratory of Computer Network and Information Integration ( Southeast University) , MOE , China; Collaborative Innovation Center for Wireless Communications Technology , China"}],"abstract":"The task of partial label (PL) learning is to learn a multi-class classifier from training examples each associated with a set of candidate labels, among which only one corresponds to the ground-truth label. It is well known that for inducing multi-class predictive model, the most straightforward solution is binary decomposition which works by either one-vs-rest or one-vs-one strategy. Nonetheless, the ground-truth label for each PL training example is concealed in its candidate label set and thus not accessible to the learning algorithm, binary decomposition cannot be directly applied under partial label learning scenario. In this paper, a novel approach is proposed to solving partial label learning problem by adapting the popular one-vs-one decomposition strategy. Specifically, one binary classifier is derived for each pair of class labels, where PL training examples with distinct relevancy to the label pair are used to generate the corresponding binary training set. After that, one binary classifier is further derived for each class label by stacking over predictions of existing binary classifiers to improve generalization. Experimental studies on both artificial and real-world PL data sets clearly validate the effectiveness of the proposed binary decomposition approach w.r.t state-of-the-art partial label learning techniques.","doi":"10.24963/ijcai.2018/398","url":"https://ijcai.org/proceedings/2018/398","pdf":"https://ijcai.org/proceedings/2018/0398.pdf","markdown":"https://ijcai.org/proceedings/2018/398.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/398","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":398,"pages":{"first":2868,"last":2874},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Amores, 2013] J. 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