{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,22]],"date-time":"2025-02-22T05:27:12Z","timestamp":1740202032774,"version":"3.37.3"},"reference-count":0,"publisher":"IOS Press","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"abstract":"<jats:p>Single-label classification associates each instance with a single label, while multi-label classification (MLC), assigns multiple labels to instances. Simple MLC systems assume that labels are independent of one another, while more complex approaches capture inter-dependencies among labels. Experiments comparing performance of MLC systems demonstrate that there is much room for improvement.<\/jats:p>","DOI":"10.3233\/978-1-61499-672-9-1336","type":"book-chapter","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:21:00Z","timestamp":1740133260000},"source":"Crossref","is-referenced-by-count":0,"title":["Improved Multi-Label Classification Using Inter-Dependence Structure via a Generative Mixture Model"],"prefix":"10.3233","author":[{"family":"Simha Ramanuja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Shatkay Hagit","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2016"],"original-title":[],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T10:55:32Z","timestamp":1740135332000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospressISBN&isbn=978-1-61499-671-2&spage=1336&doi=10.3233\/978-1-61499-672-9-1336"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/978-1-61499-672-9-1336","relation":{},"ISSN":["0922-6389"],"issn-type":[{"value":"0922-6389","type":"print"}],"subject":[],"published":{"date-parts":[[2016]]}}}