{"type":"ScholarlyArticle","title":"Generative Adversarial Positive-Unlabelled Learning","authors":[{"name":"Ming Hou","slug":"ming-hou","given":"Ming","family":"Hou","affiliation":"Tensor Learning Unit , Center for Advanced Intelligence Project , RIKEN , Japan"},{"name":"Brahim Chaib-draa","slug":"brahim-chaib-draa","given":"Brahim","family":"Chaib-draa","affiliation":"Department of Computer Science and Software Engineering , Laval University , Canada"},{"name":"Chao Li","slug":"chao-li","given":"Chao","family":"Li","affiliation":"Causal Inference Team , Center for Advanced Intelligence Project , RIKEN , Japan"},{"name":"Qibin Zhao","slug":"qibin-zhao","given":"Qibin","family":"Zhao","affiliation":"School of Automation , Guangdong University of Technology , China"}],"abstract":"In this work, we consider the task of classifying binary positive-unlabeled (PU) data. The existing discriminative learning based PU models attempt to seek an optimal reweighting strategy for U data, so that a decent decision boundary can be found. However, given limited P data, the conventional PU models tend to suffer from overfitting when adapted to very flexible deep neural networks. In contrast, we are the first to innovate a totally new paradigm to attack the binary PU task, from perspective of generative learning by leveraging the powerful generative adversarial networks (GAN). Our generative positive-unlabeled (GenPU) framework incorporates an array of discriminators and generators that are endowed with different roles in simultaneously producing positive and negative realistic samples. We provide theoretical analysis to justify that, at equilibrium, GenPU is capable of recovering both positive and negative data distributions. Moreover, we show GenPU is generalizable and closely related to the semi-supervised classification. Given rather limited P data, experiments on both synthetic and real-world dataset demonstrate the effectiveness of our proposed framework. With infinite realistic and diverse sample streams generated from GenPU, a very flexible classifier can then be trained using deep neural networks.","doi":"10.24963/ijcai.2018/312","url":"https://ijcai.org/proceedings/2018/312","pdf":"https://ijcai.org/proceedings/2018/0312.pdf","markdown":"https://ijcai.org/proceedings/2018/312.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/312","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":312,"pages":{"first":2255,"last":2261},"keywords":["Machine Learning: Semi-Supervised Learning","Machine Learning: Learning Generative Models"],"videos":[],"references":[{"position":1,"raw":"[Arjovsky et al., 2017] Martin Arjovsky, Soumith Chintala, and Léon Bottou. Wasserstein generative adversarial net- works. In International Conference on Machine Learning, pages 214-223, 2017.","title":"Wasserstein generative adversarial networks","authors":"Arjovsky","year":"2017","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Denis et al., 2005] Franc ¸ois Denis, Rémi Gilleron, and Fa- bien Letouzey. Learning from positive and unlabeled ex- amples. Theoretical Computer Science, 348(1):70-83, 2005.","title":"Franc ¸ois Denis, Rémi Gilleron, and Fabien Letouzey. Learning from positive and unlabeled examples","authors":"Denis","year":"2005","venue":"Theoretical Computer Science","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Du Plessis et al., 2014] Marthinus C Du Plessis, Gang Niu, and Masashi Sugiyama. Analysis of learning from positive and unlabeled data. In Advances in Neural Information Processing Systems, pages 703-711, 2014.","title":"Analysis of learning from positive and unlabeled data","authors":"Marthinus C Du, Gang Plessis, Masashi Niu, Sugiyama","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Du Plessis et al., 2015] Marthinus Du Plessis, Gang Niu, and Masashi Sugiyama. Convex formulation for learning from positive and unlabeled data. In International Confer- ence on Machine Learning, pages 1386-1394, 2015.","title":"Convex formulation for learning from positive and unlabeled data","authors":"Marthinus Du Plessis, Gang Niu, Masashi Sugiyama","year":"2015","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Elkan and Noto, 2008] Charles Elkan and Keith Noto. Learning classifiers from only positive and unlabeled da- ta. In SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 213-220. ACM, 2008.","title":"Learning classifiers from only positive and unlabeled data","authors":"Noto Elkan, Charles Elkan, Keith Noto","year":"2008","venue":"SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In Advances in Neural Informa- tion Processing Systems, pages 2672-2680, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Goodfellow et al., 2016] Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio. Deep learning. MIT press Cambridge, 2016.","title":"Deep learning","authors":"Ian Goodfellow, Yoshua Bengio, Aaron Courville, Yoshua Bengio","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gulrajani et al., 2017] Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville. Improved training of wasserstein GANs. In Ad- vances in Neural Information Processing Systems, pages 5769-5779, 2017.","title":"Improved training of wasserstein GANs","authors":"Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Aaron C Vincent Dumoulin, Courville","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hido et al., 2008] Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, and Takafumi Kanamori. Inlier-based outlier detection via direct density ratio es- timation. In International Conference on Data Mining, pages 223-232. IEEE, 2008.","title":"Inlier-based outlier detection via direct density ratio estimation","authors":"Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, Takafumi Kanamori","year":"2008","venue":"International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jain et al., 2016] Shantanu Jain, Martha White, and Predrag Radivojac. Estimating the class prior and posterior from noisy positives and unlabeled data. In Advances in Neural Information Processing Systems, pages 2693-2701, 2016.","title":"Estimating the class prior and posterior from noisy positives and unlabeled data","authors":"Shantanu Jain, Martha White, Predrag Radivojac","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kiryo et al., 2017] Ryuichi Kiryo, Gang Niu, Marthinus C du Plessis, and Masashi Sugiyama. Positive-unlabeled learning with non-negative risk estimator. In Advances in Neural Information Processing Systems, pages 1674- 1684, 2017.","title":"Positive-unlabeled learning with non-negative risk estimator","authors":"Ryuichi Kiryo, Gang Niu, C Marthinus, Masashi Du Plessis, Sugiyama","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[LeCun et al., 1990] Yann LeCun, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, and Lawrence D Jackel. Handwritten digit recognition with a back-propagation network. In Advances in Neural Information Processing Systems, pages 396- 404, 1990.","title":"Handwritten digit recognition with a back-propagation network","authors":"Yann Lecun, Bernhard E Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne E Hubbard, Lawrence D Jackel","year":"1990","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[LeCun et al., 1998] Yann LeCun, Corinna Cortes, and Christopher JC Burges. The MNIST database of hand- written digits. http://yann.lecun.com/exdb/mnist/, 1998.","title":"The MNIST database of handwritten digits","authors":"Yann Lecun, Corinna Cortes, J C Christopher, Burges","year":null,"venue":null,"doi":null,"url":"http://yann.lecun.com/exdb/mnist/,1998","internal_url":null},{"position":14,"raw":"[Lee and Liu, 2003] Wee Sun Lee and Bing Liu. Learning with positive and unlabeled examples using weighted lo- gistic regression. In International Conference on Machine Learning, pages 448-455, 2003.","title":"Learning with positive and unlabeled examples using weighted logistic regression","authors":"Lee, Liu ; Wee, Sun Lee, Bing Liu","year":"2003","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li and Liu, 2003] Xiaoli Li and Bing Liu. Learning to clas- sify texts using positive and unlabeled data. In Internation- al Joint Conference on Artifical Intelligence, pages 587- 592, 2003.","title":"Learning to classify texts using positive and unlabeled data","authors":"Li, Liu ; ] Xiaoli Li, Bing Liu","year":"2003","venue":"International Joint Conference on Artifical Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2003/87"},{"position":16,"raw":"[Li et al., 2011] Wenkai Li, Qinghua Guo, and Charles Elka- n. A positive and unlabeled learning algorithm for one- class classification of remote-sensing data. IEEE Transac- tions on Geoscience and Remote Sensing, 49(2):717-725, 2011.","title":"A positive and unlabeled learning algorithm for oneclass classification of remote-sensing data","authors":"Li","year":"2011","venue":"IEEE Transactions on Geoscience and Remote Sensing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2002] Bing Liu, Wee Sun Lee, Philip S Yu, and Xiaoli Li. Partially supervised classification of text docu- ments. In International Conference on Machine Learning, pages 387-394, 2002.","title":"Partially supervised classification of text documents","authors":"Bing Liu, Wee Sun Lee, Philip S Yu, Xiaoli Li","year":"2002","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2003] Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, and Philip S Yu. Building text classifiers using posi- tive and unlabeled examples. In International Conference on Data Mining, pages 179-186. IEEE, 2003.","title":"Building text classifiers using positive and unlabeled examples","authors":"Bing Liu, Yang Dai, Xiaoli Li, Wee Sun Lee, Philip S Yu","year":"2003","venue":"International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu et al., 2015] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In International Conference on Computer Vision, pages 3730-3738, 2015.","title":"Deep learning face attributes in the wild","authors":"Ziwei Liu, Ping Luo, Xiaogang Wang, Xiaoou Tang","year":"2015","venue":"International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Patrini et al., 2016] Giorgio Patrini, Frank Nielsen, Richard Nock, and Marcello Carioni. Loss factorization, weakly supervised learning and label noise robustness. In Interna- tional Conference on Machine Learning, pages 708-717, 2016.","title":"Loss factorization, weakly supervised learning and label noise robustness","authors":"Giorgio Patrini, Frank Nielsen, Richard Nock, Marcello Carioni","year":"2016","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Salimans et al., 2016] Tim Salimans, Ian Goodfellow, Woj- ciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training GANs. In Advances in Neural Information Processing Systems, pages 2234- 2242, 2016.","title":"Improved techniques for training GANs","authors":"Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen","year":"2016","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Ward et al., 2009] Gill Ward, Trevor Hastie, Simon Barry, Jane Elith, and John R Leathwick. Presence-only data and the EM algorithm. Biometrics, 65(2):554-563, 2009.","title":"Presence-only data and the EM algorithm","authors":"Gill Ward, Trevor Hastie, Simon Barry, Jane Elith, John R Leathwick","year":"2009","venue":"Biometrics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}