{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,27]],"date-time":"2026-09-27T06:44:29Z","timestamp":1790491469421,"version":"4.1.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>\u00a0In 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.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/312","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:49:10Z","timestamp":1530769750000},"page":"2255-2261","source":"Crossref","is-referenced-by-count":44,"title":["Generative Adversarial Positive-Unlabelled Learning"],"prefix":"10.24963","author":[{"given":"Ming","family":"Hou","sequence":"first","affiliation":[{"name":"Tensor Learning Unit, Center for Advanced Intelligence Project, RIKEN, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brahim","family":"Chaib-draa","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Laval University, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"Causal Inference Team, Center for Advanced Intelligence Project, RIKEN, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qibin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Tensor Learning Unit, Center for Advanced Intelligence Project, RIKEN, Japan"},{"name":"School of Automation, Guangdong University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T05:51:39Z","timestamp":1530769899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/312"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/312","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}