{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T04:28:55Z","timestamp":1744172935543,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030739720"},{"type":"electronic","value":"9783030739737"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-73973-7_3","type":"book-chapter","created":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T14:03:24Z","timestamp":1617977004000},"page":"24-33","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Metric Learning for Multi-label Classification"],"prefix":"10.1007","author":[{"given":"Marco","family":"Brighi","sequence":"first","affiliation":[]},{"given":"Annalisa","family":"Franco","sequence":"additional","affiliation":[]},{"given":"Dario","family":"Maio","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,10]]},"reference":[{"issue":"9","key":"3_CR1","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","volume":"37","author":"MR Boutell","year":"2004","unstructured":"Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757\u20131771 (2004)","journal-title":"Pattern Recogn."},{"issue":"1","key":"3_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"3_CR3","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1007\/978-3-642-37456-2_14","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"RJGB Campello","year":"2013","unstructured":"Campello, R.J.G.B., Moulavi, D., Sander, J.: Density-based clustering based on hierarchical density estimates. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013. LNCS (LNAI), vol. 7819, pp. 160\u2013172. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-37456-2_14"},{"issue":"1","key":"3_CR4","doi-asserted-by":"publisher","first-page":"4","DOI":"10.19153\/cleiej.14.1.4","volume":"14","author":"EA Cherman","year":"2011","unstructured":"Cherman, E.A., Monard, M.C., Metz, J.: Multi-label problem transformation methods: a case study. CLEI Electron. J. 14(1), 4\u20134 (2011)","journal-title":"CLEI Electron. J."},{"issue":"1","key":"3_CR5","first-page":"19","volume":"5","author":"D Ganda","year":"2018","unstructured":"Ganda, D., Buch, R.: A survey on multi label classification. Recent Trends Program. Lang. 5(1), 19\u201323 (2018)","journal-title":"Recent Trends Program. Lang."},{"key":"3_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1007\/978-3-540-24775-3_5","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"S Godbole","year":"2004","unstructured":"Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 22\u201330. Springer, Heidelberg (2004). https:\/\/doi.org\/10.1007\/978-3-540-24775-3_5"},{"key":"3_CR7","first-page":"513","volume":"17","author":"J Goldberger","year":"2005","unstructured":"Goldberger, J., Hinton, G.E., Roweis, S.T., Salakhutdinov, R.R.: Neighbourhood components analysis. Adv. Neural Info. Process. Syst. 17, 513\u2013520 (2005)","journal-title":"Adv. Neural Info. Process. Syst."},{"key":"3_CR8","unstructured":"Gouk, H., Pfahringer, B., Cree, M.: Learning distance metrics for multi-label classification. In: Asian Conference on Machine Learning, pp. 318\u2013333. PMLR (2016)"},{"key":"3_CR9","unstructured":"Gupta, P., Anand, A.: Multi label classification using label clustering. In: Appearing in Proceedings of the 1st Indian Workshop on Machine Learning, IIT Kanpur, India (2013)"},{"key":"3_CR10","unstructured":"Katakis, I., Tsoumakas, G., Vlahavas, I.: Multilabel text classification for automated tag suggestion. In: Proceedings of the ECML\/PKDD, vol. 18, p. 5 (2008)"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Liu, W., Tsang, I.W.: Large margin metric learning for multi-label prediction. In: Proceedings of the National Conference on Artificial Intelligence (2015)","DOI":"10.1609\/aaai.v29i1.9610"},{"issue":"9","key":"3_CR12","doi-asserted-by":"publisher","first-page":"3084","DOI":"10.1016\/j.patcog.2012.03.004","volume":"45","author":"G Madjarov","year":"2012","unstructured":"Madjarov, G., Kocev, D., Gjorgjevikj, D., D\u017eeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084\u20133104 (2012)","journal-title":"Pattern Recogn."},{"key":"3_CR13","series-title":"Lecture Notes in ComputeR science (lecture notes in artificial intelligence)","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-662-44851-9_28","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"J Nam","year":"2014","unstructured":"Nam, J., Kim, J., Loza Menc\u00eda, E., Gurevych, I., F\u00fcrnkranz, J.: Large-scale multi-label text classification \u2014 revisiting neural networks. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 437\u2013452. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44851-9_28"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Nasierding, G., Tsoumakas, G., Kouzani, A.Z.: Clustering based multi-label classification for image annotation and retrieval. In: IEEE SMC, pp. 4514\u20134519 (2009)","DOI":"10.1109\/ICSMC.2009.5346902"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Read, J., Pfahringer, B., Holmes, G.: Multi-label classification using ensembles of pruned sets. In: IEEE ICDM, pp. 995\u20131000. IEEE (2008)","DOI":"10.1109\/ICDM.2008.74"},{"key":"3_CR16","first-page":"1027","volume":"8","author":"M Sugiyama","year":"2007","unstructured":"Sugiyama, M.: Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J. Mach. Learn. Res. 8, 1027\u20131061 (2007)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"406","DOI":"10.1007\/978-3-540-74958-5_38","volume-title":"Machine Learning: ECML 2007","author":"G Tsoumakas","year":"2007","unstructured":"Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladeni\u010d, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406\u2013417. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-74958-5_38"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: a unified framework for multi-label image classification. In: IEEE CVPR (2016)","DOI":"10.1109\/CVPR.2016.251"},{"issue":"2","key":"3_CR19","first-page":"1","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10(2), 1\u201338 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Yu, Z., Wang, Q., Fan, Y., Dai, H., Qiu, M.: An improved classifier chain algorithm for multi-label classification of big data analysis. In: 2015 IEEE 17th HPCC, 2015 IEEE 7th CSS, and 2015 IEEE 12th ICESS, pp. 1298\u20131301 (2015)","DOI":"10.1109\/HPCC-CSS-ICESS.2015.240"},{"issue":"7","key":"3_CR21","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang, M.L., Zhou, Z.H.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038\u20132048 (2007)","journal-title":"Pattern Recogn."}],"container-title":["Lecture Notes in Computer Science","Structural, Syntactic, and Statistical Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73973-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T22:02:33Z","timestamp":1744149753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-73973-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030739720","9783030739737"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73973-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"10 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"S+SSPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 January 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 January 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sspr2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dais.unive.it\/sspr2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"81","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"35","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}