{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:38:24Z","timestamp":1743057504514,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030916077"},{"type":"electronic","value":"9783030916084"}],"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\/978-3-030-91608-4_57","type":"book-chapter","created":{"date-parts":[[2021,11,23]],"date-time":"2021-11-23T20:05:55Z","timestamp":1637697955000},"page":"587-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Developments on Support Vector Machines for Multiple-Expert Learning"],"prefix":"10.1007","author":[{"given":"Ana C.","family":"Umaquinga-Criollo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan D.","family":"Tamayo-Quintero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mar\u00eda N.","family":"Moreno-Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yahya","family":"Aalaila","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diego H.","family":"Peluffo-Ord\u00f3\u00f1ez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,11,23]]},"reference":[{"key":"57_CR1","doi-asserted-by":"crossref","unstructured":"Alzubi, J., Nayyar, A., Kumar, A.: Machine learning from theory to algorithms: an overview. In: Journal of Physics: Conference Series, vol. 1142, p. 012012. IOP Publishing (2018)","DOI":"10.1088\/1742-6596\/1142\/1\/012012"},{"key":"57_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-030-01364-6_6","volume-title":"Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis","author":"V Chang","year":"2018","unstructured":"Chang, V., et al.: Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts. In: Stoyanov, D. (ed.) LABELS\/CVII\/STENT -2018. LNCS, vol. 11043, pp. 45\u201354. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01364-6_6"},{"key":"57_CR3","unstructured":"Danenas, P., Garsva, G., Simutis, R.: Development of discriminant analysis and majority-voting based credit risk assessment classifier. In: Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, vol. 1, pp. 204\u2013209 (2011)"},{"key":"57_CR4","doi-asserted-by":"publisher","unstructured":"Dekel, O., Shamir, O.: Good learners for evil teachers. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 233\u2013240. Association for Computing Machinery, New York (2009). https:\/\/doi.org\/10.1145\/1553374.1553404","DOI":"10.1145\/1553374.1553404"},{"key":"57_CR5","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.patrec.2018.10.005","volume":"116","author":"J Gil-Gonzalez","year":"2018","unstructured":"Gil-Gonzalez, J., Alvarez-Meza, A., Orozco-Gutierrez, A.: Learning from multiple annotators using kernel alignment. Pattern Recogn. Lett. 116, 150\u2013156 (2018). https:\/\/doi.org\/10.1016\/j.patrec.2018.10.005","journal-title":"Pattern Recogn. Lett."},{"issue":"2","key":"57_CR6","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"CW Hsu","year":"2002","unstructured":"Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. Neural Netw. IEEE Trans. 13(2), 415\u2013425 (2002)","journal-title":"Neural Netw. IEEE Trans."},{"key":"57_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zheng, Y.F.: One-against-all multi-class SVM classification using reliability measures. In: IEEE International Joint Conference on Neural Networks, vol. 2, pp. 849\u2013854. IEEE (2005)","DOI":"10.1109\/IJCNN.2005.1555963"},{"key":"57_CR8","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.cviu.2016.01.006","volume":"151","author":"D Mahapatra","year":"2016","unstructured":"Mahapatra, D.: Combining multiple expert annotations using semi-supervised learning and graph cuts for medical image segmentation. Comput. Vis. Image Underst. 151, 114\u2013123 (2016). https:\/\/doi.org\/10.1016\/j.cviu.2016.01.006","journal-title":"Comput. Vis. Image Underst."},{"key":"57_CR9","unstructured":"Murillo, S., Peluffo, D.H., Castellanos, G.: Support vector machine-based approach for multi-labelers problems. In: European Symposium on Artificial Neural Networks, Computational Inteligence and Machine Learning (2013)"},{"key":"57_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1007\/978-3-642-38637-4_28","volume-title":"Natural and Artificial Models in Computation and Biology","author":"S Murillo-Rend\u00f3n","year":"2013","unstructured":"Murillo-Rend\u00f3n, S., Peluffo-Ord\u00f3\u00f1ez, D., Arias-Londo\u00f1o, J.D., Castellanos-Dom\u00ednguez, C.G.: Multi-labeler analysis for bi-class problems based on soft-margin support vector machines. In: Ferr\u00e1ndez Vicente, J.M., \u00c1lvarez S\u00e1nchez, J.R., de la Paz L\u00f3pez, F., Toledo Moreo, F.J. (eds.) IWINAC 2013. LNCS, vol. 7930, pp. 274\u2013282. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38637-4_28"},{"key":"57_CR11","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.media.2018.09.005","volume":"50","author":"G Nir","year":"2018","unstructured":"Nir, G., et al.: Automatic grading of prostate cancer in digitized histopathology images learning from multiple experts. Med. Image Anal. 50, 167\u2013180 (2018)","journal-title":"Med. Image Anal."},{"key":"57_CR12","unstructured":"Peluffo-Ord\u00f3\u00f1ez, D.H., Rend\u00f3n, S.M., Arias-Londo\u00f1o, J.D., Castellanos-Dom\u00ednguez, G.: A multi-class extension for multi-labeler support vector machines. In: European Symposium on Artificial Neural Networks, Computational Inteligence and Machine Learning (2014)"},{"key":"57_CR13","unstructured":"Raykar, V., et al.: Learning from crowds. J. Mach. Learn. Res. 11, 1297\u20131322 (2010). http:\/\/jmlr.org\/papers\/v11\/raykar10a.html"},{"key":"57_CR14","doi-asserted-by":"publisher","unstructured":"Raykar, V.C., et al.: Supervised learning from multiple experts : whom to trust when everyone lies a bit. In: ACM International Conference Proceeding Series. vol. 382, pp. 1\u20138. ACM Press, New York (2009). https:\/\/doi.org\/10.1145\/1553374.1553488, http:\/\/portal.acm.org\/citation.cfm?doid=1553374.1553488","DOI":"10.1145\/1553374.1553488"},{"issue":"12","key":"57_CR15","doi-asserted-by":"publisher","first-page":"1428","DOI":"10.1016\/j.patrec.2013.05.012","volume":"34","author":"F Rodrigues","year":"2013","unstructured":"Rodrigues, F., Pereira, F., Ribeiro, B.: Learning from multiple annotators: distinguishing good from random labelers. Pattern Recogn. Lett. 34(12), 1428\u20131436 (2013). https:\/\/doi.org\/10.1016\/j.patrec.2013.05.012","journal-title":"Pattern Recogn. Lett."},{"key":"57_CR16","unstructured":"Rodrigues, F., Pereira, F., Ribeiro, B.: Gaussian process classification and active learning with multiple annotators. In: Xing, E.P., Jebara, T. (eds.) Proceedings of the 31st International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 32, pp. 433\u2013441. PMLR, Bejing, China (22\u201324 Jun 2014). http:\/\/proceedings.mlr.press\/v32\/rodrigues14.html"},{"key":"57_CR17","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/4175.001.0001","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)"},{"key":"57_CR18","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)","DOI":"10.1017\/CBO9781107298019"},{"key":"57_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-030-61705-9_36","volume-title":"Hybrid Artificial Intelligent Systems","author":"AC Umaquinga-Criollo","year":"2020","unstructured":"Umaquinga-Criollo, A.C., Tamayo-Quintero, J.D., Moreno-Garc\u00eda, M.N., Riascos, J.A., Peluffo-Ord\u00f3\u00f1ez, D.H.: Multi-expert methods evaluation on financial and economic data: introducing bag of experts. In: de la Cal, E.A., Villar Flecha, J.R., Quinti\u00e1n, H., Corchado, E. (eds.) HAIS 2020. LNCS (LNAI), vol. 12344, pp. 437\u2013449. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61705-9_36"},{"issue":"3","key":"57_CR20","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s10994-013-5412-1","volume":"95","author":"Y Yan","year":"2014","unstructured":"Yan, Y., Rosales, R., Fung, G., Subramanian, R., Dy, J.: Learning from multiple annotators with varying expertise. Mach. Learn. 95(3), 291\u2013327 (2014). https:\/\/doi.org\/10.1007\/s10994-013-5412-1","journal-title":"Mach. Learn."},{"issue":"2","key":"57_CR21","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1109\/TKDE.2014.2327039","volume":"27","author":"J Zhang","year":"2015","unstructured":"Zhang, J., Wu, X., Sheng, V.S.: Imbalanced multiple noisy labeling. IEEE Trans. Knowl. Data Eng. 27(2), 489\u2013503 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-91608-4_57","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T22:14:13Z","timestamp":1726179253000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-91608-4_57"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030916077","9783030916084"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-91608-4_57","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":"23 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","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":"25 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ideal-conf.com\/ideal2021","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":"85","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":"61","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":"72% - 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":"2.8","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":"2.3","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 took place virtually 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)"}}]}}