{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T07:36:23Z","timestamp":1723016183252},"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":[[2020,7]]},"abstract":"<jats:p>Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets.\n\nDiscovering and exploring the latent labels hidden in the data may not only \n\nfind interesting knowledge but also help us to build a more robust learning model.\n\nIn this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously.\n\nExtensive experiments show a competitive performance of DLCL against other \n\nstate-of-the-art MLL approaches.<\/jats:p>","DOI":"10.24963\/ijcai.2020\/423","type":"proceedings-article","created":{"date-parts":[[2020,7,8]],"date-time":"2020-07-08T12:12:10Z","timestamp":1594210330000},"page":"3058-3064","source":"Crossref","is-referenced-by-count":5,"title":["Discovering Latent Class Labels for Multi-Label Learning"],"prefix":"10.24963","author":[{"given":"Jun","family":"Huang","sequence":"first","affiliation":[{"name":"Graduate School of Information Science and Technology, The University of Tokyo"},{"name":"School of Computer Science and Technology, Anhui University of Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linchuan","family":"Xu","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, The University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Greenwich"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanyang Technological University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenji","family":"Yamanishi","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, The University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-PRICAI-2020","name":"Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}","start":{"date-parts":[[2020,7,11]]},"theme":"Artificial Intelligence","location":"Yokohama, Japan","end":{"date-parts":[[2020,7,17]]}},"container-title":["Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2020,7,9]],"date-time":"2020-07-09T02:15:04Z","timestamp":1594260904000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2020\/423"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2020\/423","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}