{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:21:03Z","timestamp":1742923263570,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030617042"},{"type":"electronic","value":"9783030617059"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-61705-9_36","type":"book-chapter","created":{"date-parts":[[2020,11,4]],"date-time":"2020-11-04T16:03:54Z","timestamp":1604505834000},"page":"437-449","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-expert Methods Evaluation on Financial and Economic Data: Introducing Bag of Experts"],"prefix":"10.1007","author":[{"given":"A. C.","family":"Umaquinga-Criollo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. D.","family":"Tamayo-Quintero","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. N.","family":"Moreno-Garc\u00eda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J. A.","family":"Riascos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D. H.","family":"Peluffo-Ord\u00f3\u00f1ez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,4]]},"reference":[{"issue":"5","key":"36_CR1","first-page":"3786","volume":"9","author":"G Attigeri","year":"2019","unstructured":"Attigeri, G., Manohara Pai, M., Pai, R.: Framework to predict NPA\/willful defaults in corporate loans: a big data approach. Int. J. Electr. Comput. Eng. 9(5), 3786\u20133797 (2019)","journal-title":"Int. J. Electr. Comput. Eng."},{"issue":"4","key":"36_CR2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/45.329294","volume":"13","author":"G Bebis","year":"1994","unstructured":"Bebis, G., Georgiopoulos, M.: Feed-forward neural networks. IEEE Potentials 13(4), 27\u201331 (1994)","journal-title":"IEEE Potentials"},{"key":"36_CR3","unstructured":"Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Wortman, J.: Learning bounds for domain adaptation (2009)"},{"key":"36_CR4","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.: Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts. In: Stoyanov, D., et al. (eds.) 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":"36_CR5","first-page":"1757","volume":"9","author":"K Crammer","year":"2008","unstructured":"Crammer, K., Kearns, M., Wortman, J.: Learning from multiple sources. J. Mach. Learn. Res. 9, 1757\u20131774 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"36_CR6","unstructured":"Danenas, P., Garsva, G., Simutis, R.: Development of discriminant analysis and majority-voting based credit risk assessment classifier. vol. 1, pp. 204\u2013209 (2011)"},{"key":"36_CR7","doi-asserted-by":"crossref","unstructured":"Dekel, O., Shamir, O.: Good Learners for Evil Teachers. Association for Computing Machinery, New York (2009)","DOI":"10.1145\/1553374.1553404"},{"key":"36_CR8","doi-asserted-by":"crossref","unstructured":"Donmez, P., Carbonell, J.G., Schneider, J.: Efficiently Learning the Accuracy of Labeling Sources for Selective Sampling. Association for Computing Machinery, New York (2009)","DOI":"10.1145\/1557019.1557053"},{"key":"36_CR9","unstructured":"Dua, D., Graff, C.: UCI machine learning repository"},{"key":"36_CR10","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)","journal-title":"Pattern Recogn. Lett."},{"key":"36_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-642-21738-8_21","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2011","author":"P Groot","year":"2011","unstructured":"Groot, P., Birlutiu, A., Heskes, T.: Learning from multiple annotators with Gaussian processes. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6792, pp. 159\u2013164. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21738-8_21"},{"issue":"2","key":"36_CR12","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.eswa.2005.09.020","volume":"31","author":"MJ Kim","year":"2006","unstructured":"Kim, M.J., Min, S.H., Han, I.: An evolutionary approach to the combination of multiple classifiers to predict a stock price index. Expert Syst. Appl. 31(2), 241\u2013247 (2006)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"36_CR13","doi-asserted-by":"publisher","first-page":"43","DOI":"10.4018\/IJSIR.2016070103","volume":"7","author":"G Klepac","year":"2016","unstructured":"Klepac, G.: Customer profiling in complex analytical environments using swarm intelligence algorithms. Int. J. Swarm Intell. Res. (IJSIR) 7(3), 43\u201370 (2016)","journal-title":"Int. J. Swarm Intell. Res. (IJSIR)"},{"key":"36_CR14","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.eswa.2018.09.005","volume":"117","author":"T Lee","year":"2019","unstructured":"Lee, T., Cho, J., Kwon, D., Sohn, S.: Global stock market investment strategies based on financial network indicators using machine learning techniques. Expert Syst. Appl. 117, 228\u2013242 (2019)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"36_CR15","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/s11263-015-0834-9","volume":"116","author":"C Long","year":"2016","unstructured":"Long, C., Hua, G., Kapoor, A.: A joint gaussian process model for active visual recognition with expertise estimation in crowdsourcing. Int. J. Comput. Vis. 116(2), 136\u2013160 (2016)","journal-title":"Int. J. Comput. Vis."},{"key":"36_CR16","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)","journal-title":"Comput. Vis. Image Underst."},{"key":"36_CR17","unstructured":"Murillo Rend\u00f3n, S.: Metodolog\u00eda para el aprendizaje de m\u00e1quina a partir de m\u00faltiples expertos en procesos de clasificaci\u00f3n de biose\u00f1ales. Ph.D. thesis"},{"key":"36_CR18","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."},{"issue":"2 Special Issue","key":"36_CR19","first-page":"674","volume":"11","author":"S Patwardhan","year":"2019","unstructured":"Patwardhan, S., Yadav, D., Parlikar, S.: A review of role of data mining techniques in portfolio management. J. Adv. Res. Dyn. Control Syst. 11(2 Special Issue), 674\u2013681 (2019)","journal-title":"J. Adv. Res. Dyn. Control Syst."},{"key":"36_CR20","unstructured":"Peluffo-Ord\u00f3\u00f1ez, D., Murillo-Rend\u00f3n, S., Arias-Londo\u00f1o, J., Castellanos-Dom\u00ednguez, G.: A multi-class extension for multi-labeler support vector machines, pp. 701\u2013706 (2014)"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Raykar, V., et al.: Supervised learning from multiple experts : whom to trust when everyone lies a bit, vol. 382 (2009)","DOI":"10.1145\/1553374.1553488"},{"key":"36_CR22","first-page":"1297","volume":"11","author":"V Raykar","year":"2010","unstructured":"Raykar, V., et al.: Learning from crowds. J. Mach. Learn. Res. 11, 1297\u20131322 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"12","key":"36_CR23","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)","journal-title":"Pattern Recogn. Lett."},{"key":"36_CR24","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 PMLR, Bejing, China, 22\u201324 June 2014, vol. 32, pp. 433\u2013441 (2014)"},{"issue":"6","key":"36_CR25","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1016\/j.jbi.2013.08.007","volume":"46","author":"H Valizadegan","year":"2013","unstructured":"Valizadegan, H., Nguyen, Q., Hauskrecht, M.: Learning classification models from multiple experts. J. Biomed. Inform. 46(6), 1125\u20131135 (2013)","journal-title":"J. Biomed. Inform."},{"issue":"19","key":"36_CR26","doi-asserted-by":"publisher","first-page":"2488","DOI":"10.1007\/s11434-012-5133-z","volume":"57","author":"W Wang","year":"2012","unstructured":"Wang, W., Zhou, Z.: Learnability of multi-instance multi-label learning. Chin. Sci. Bull. 57(19), 2488\u20132491 (2012)","journal-title":"Chin. Sci. Bull."},{"key":"36_CR27","doi-asserted-by":"crossref","unstructured":"Wiebe, J., Mihalcea, R.: Word sense and subjectivity, vol. 1, pp. 1065\u20131072 (2006)","DOI":"10.3115\/1220175.1220309"},{"issue":"3","key":"36_CR28","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s10994-013-5412-1","volume":"95","author":"Y Yan","year":"2013","unstructured":"Yan, Y., Rosales, R., Fung, G., Subramanian, R., Dy, J.: Learning from multiple annotators with varying expertise. Mach. Learn. 95(3), 291\u2013327 (2013). https:\/\/doi.org\/10.1007\/s10994-013-5412-1","journal-title":"Mach. Learn."},{"key":"36_CR29","doi-asserted-by":"publisher","first-page":"113041","DOI":"10.1016\/j.eswa.2019.113041","volume":"143","author":"H Yun","year":"2020","unstructured":"Yun, H., Lee, M., Kang, Y., Seok, J.: Portfolio management via two-stage deep learning with a joint cost. Expert Syst. Appl. 143, 113041 (2020)","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"36_CR30","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."},{"issue":"4","key":"36_CR31","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TII.2018.2791424","volume":"15","author":"Q Zhang","year":"2019","unstructured":"Zhang, Q., Yang, L.T., Chen, Z., Li, P., Bu, F.: An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing. IEEE Trans. Ind. Inform. 15(4), 2330\u20132337 (2019)","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61705-9_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T22:32:41Z","timestamp":1723847561000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61705-9_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030617042","9783030617059"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61705-9_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"4 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gij\u00f3n","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.haisconference.eu\/","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":"106","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":"65","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":"61% - 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":"2","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)"}}]}}