{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:11:57Z","timestamp":1762956717245,"version":"3.44.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030865221"},{"type":"electronic","value":"9783030865238"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86523-8_28","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T06:05:16Z","timestamp":1631253916000},"page":"462-477","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Gradient-Based Label Binning in Multi-label Classification"],"prefix":"10.1007","author":[{"given":"Michael","family":"Rapp","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eneldo Loza","family":"Menc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"F\u00fcrnkranz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eyke","family":"H\u00fcllermeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,11]]},"reference":[{"key":"28_CR1","unstructured":"Amit, Y., Dekel, O., Singer, Y.: A boosting algorithm for label covering in multilabel problems. In: Proceedings of 11th International Conference on AI and Statistics (AISTATS), pp. 27\u201334 (2007)"},{"key":"28_CR2","unstructured":"Bhatia, K., Jain, H., Kar, P., Varma, M., Jain, P.: Sparse local embeddings for extreme multi-label classification. In: Proceedings of 28th International Conference on Neural Information Processing Systems (NIPS), pp. 730\u2013738 (2015)"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of 22nd International Conference on Knowledge Discovery and Data Mining (KDD), pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"28_CR4","unstructured":"Cheng, W., H\u00fcllermeier, E., Dembczy\u0144ski, K.: Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of 27th International Conference on Machine Learning (ICML), pp. 279\u2013286 (2010)"},{"key":"28_CR5","unstructured":"Dembczy\u0144ski, K., Kot\u0142owski, W., H\u00fcllermeier, E.: Consistent multilabel ranking through univariate losses. In: Proceedings of 29th International Conference on Machine Learning (ICML), pp. 1319\u20131326 (2012)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Dembczy\u0144ski, K., Waegeman, W., Cheng, W., H\u00fcllermeier, E.: On label dependence and loss minimization in multi-label classification. Mach. Learn. 88(1-2), 5\u201345 (2012)","DOI":"10.1007\/s10994-012-5285-8"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"F\u00fcrnkranz, J., Gamberger, D., Lavra\u010d, N.: Foundations of Rule Learning. Springer Science & Business Media (2012)","DOI":"10.1007\/978-3-540-75197-7"},{"issue":"6","key":"28_CR8","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1002\/widm.1139","volume":"4","author":"E Gibaja","year":"2014","unstructured":"Gibaja, E., Ventura, S.: Multi-label learning: a review of the state of the art and ongoing research. Wiley Interdisciplinary Rev. Data Mining Knowl. Discovery 4(6), 411\u2013444 (2014)","journal-title":"Wiley Interdisciplinary Rev. Data Mining Knowl. Discovery"},{"key":"28_CR9","doi-asserted-by":"crossref","unstructured":"Huang, K.H., Lin, H.T.: Cost-sensitive label embedding for multi-label classification. Mach. Learn. 106(9), 1725\u20131746 (2017)","DOI":"10.1007\/s10994-017-5659-z"},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Johnson, M., Cipolla, R.: Improved image annotation and labelling through multi-label boosting. In: Proceedings of British Machine Vision Conference (BMVC) (2005)","DOI":"10.5244\/C.19.68"},{"key":"28_CR11","unstructured":"Jung, Y.H., Tewari, A.: Online boosting algorithms for multi-label ranking. In: Proceedings of 21st International Conference on AI and Statistics (AISTATS), pp. 279\u2013287 (2018)"},{"key":"28_CR12","unstructured":"Ke, G., et al.: LightGBM: a highly efficient gradient boosting decision tree. In: Proceedings of 31st International Conference on Neural Information Processing Systems (NIPS) (2017)"},{"key":"28_CR13","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.patcog.2019.01.009","volume":"90","author":"V Kumar","year":"2019","unstructured":"Kumar, V., Pujari, A.K., Padmanabhan, V., Kagita, V.R.: Group preserving label embedding for multi-label classification. Pattern Recogn. 90, 23\u201334 (2019)","journal-title":"Pattern Recogn."},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: a fast scalable classifier for data mining. In: Proceedings of International Conference on Extending Database Technology, pp. 18\u201332 (1996)","DOI":"10.1007\/BFb0014141"},{"key":"28_CR15","doi-asserted-by":"crossref","unstructured":"Rapp, M., Loza Menc\u00eda, E., F\u00fcrnkranz, J., Nguyen, V.L., H\u00fcllermeier, E.: Learning gradient boosted multi-label classification rules. In: Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pp. 124\u2013140 (2020)","DOI":"10.1007\/978-3-030-67664-3_8"},{"key":"28_CR16","doi-asserted-by":"crossref","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pp. 254\u2013269 (2009)","DOI":"10.1007\/978-3-642-04174-7_17"},{"issue":"2","key":"28_CR17","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1023\/A:1007649029923","volume":"39","author":"RE Schapire","year":"2000","unstructured":"Schapire, R.E., Singer, Y.: BoosTexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135\u2013168 (2000)","journal-title":"Mach. Learn."},{"key":"28_CR18","unstructured":"Si, S., Zhang, H., Keerthi, S.S., Mahajan, D., Dhillon, I.S., Hsieh, C.J.: Gradient boosted decision trees for high dimensional sparse output. In: Proceedings of 34th International Conference on Machine Learning (ICML) pp. 3182\u20133190 (2017)"},{"issue":"1","key":"28_CR19","first-page":"194","volume":"33","author":"L Sun","year":"2010","unstructured":"Sun, L., Ji, S., Ye, J.: Canonical correlation analysis for multilabel classification: a least-squares formulation, extensions, and analysis. IEEE Trans. Pattern Anal. Mach. Intell. 33(1), 194\u2013200 (2010)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"9","key":"28_CR20","doi-asserted-by":"publisher","first-page":"2508","DOI":"10.1162\/NECO_a_00320","volume":"24","author":"F Tai","year":"2012","unstructured":"Tai, F., Lin, H.T.: Multilabel classification with principal label space transformation. Neural Comput. 24(9), 2508\u20132542 (2012)","journal-title":"Neural Comput."},{"key":"28_CR21","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Effective and efficient multilabel classification in domains with large number of labels. In: Proceedings of ECML-PKDD 2008 Workshop on Mining Multidimensional Data, pp. 53\u201359 (2008)"},{"key":"28_CR22","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/978-0-387-09823-4_34","volume-title":"Data Mining and Knowledge Discovery Handbook","author":"G Tsoumakas","year":"2010","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, pp. 667\u2013685. Springer, Boston (2010). https:\/\/doi.org\/10.1007\/978-0-387-09823-4_34"},{"key":"28_CR23","doi-asserted-by":"crossref","unstructured":"Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Proceedings of European Conference on Machine Learning (ECML), pp. 406\u2013417 (2007)","DOI":"10.1007\/978-3-540-74958-5_38"},{"issue":"8","key":"28_CR24","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2014","unstructured":"Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819\u20131837 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"28_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Jung, C.: GBDT-MO: gradient-boosted decision trees for multiple outputs. IEEE Trans. Neural Networks Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2020.3009776"},{"issue":"1\u20132","key":"28_CR26","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/s10994-011-5276-1","volume":"88","author":"T Zhou","year":"2012","unstructured":"Zhou, T., Tao, D., Wu, X.: Compressed labeling on distilled labelsets for multi-label learning. Mach. Learn. 88(1\u20132), 69\u2013126 (2012)","journal-title":"Mach. Learn."}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86523-8_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:02:55Z","timestamp":1757455375000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86523-8_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865221","9783030865238"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86523-8_28","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":"11 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}