{"type":"ScholarlyArticle","title":"Positive unlabeled learning via wrapper-based adaptive sampling","authors":[{"name":"Pengyi Yang","slug":"pengyi-yang","given":"Pengyi","family":"Yang","affiliation":"Charles Perkins Centre , School of Mathematics and Statistics , University of Sydney , Australia"},{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":"Advanced Analytics Institute , University of Technology Sydney , Australia"},{"name":"Jean Yang","slug":"jean-yang","given":"Jean","family":"Yang","affiliation":"Charles Perkins Centre , School of Mathematics and Statistics , University of Sydney , Australia"}],"abstract":"Learning from positive and unlabeled data frequently occurs in applications where only a subset of positive instances is available while the rest of the data are unlabeled. In such scenarios, often the goal is to create a discriminant model that can accurately classify both positive and negative data by modelling from labeled and unlabeled instances. In this study, we propose an adaptive sampling (AdaSampling) approach that utilises prediction probabilities from a model to iteratively update the training data. Starting with equal prior probabilities for all unlabeled data, our method \"wraps\" around a predictive model to iteratively update these probabilities to distinguish positive and negative instances in unlabeled data. Subsequently, one or more robust negative set(s) can be drawn from unlabeled data, according to the likelihood of each instance being negative, to train a single classification model or ensemble of models.","doi":"10.24963/ijcai.2017/457","url":"https://ijcai.org/proceedings/2017/457","pdf":"https://ijcai.org/proceedings/2017/0457.pdf","markdown":"https://ijcai.org/proceedings/2017/457.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/457","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":457,"pages":{"first":3273,"last":3279},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Ensemble Methods","Machine Learning: Classification","Machine Learning: Cost-Sensitive Learning"],"videos":[],"references":[{"position":1,"raw":"[Breiman, 1996] Leo Breiman. 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