{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T12:08:56Z","timestamp":1757592536376,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030739584"},{"type":"electronic","value":"9783030739591"}],"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\/978-3-030-73959-1_21","type":"book-chapter","created":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T15:20:40Z","timestamp":1618240840000},"page":"229-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Semi-supervised Co-ensembling for AutoML"],"prefix":"10.1007","author":[{"given":"Jesper E.","family":"van Engelen","sequence":"first","affiliation":[]},{"given":"Holger H.","family":"Hoos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,13]]},"reference":[{"key":"21_CR1","first-page":"281","volume":"13","author":"J Bergstra","year":"2012","unstructured":"Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281\u2013305 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR2","unstructured":"Bischl, B., et al.: OpenML benchmarking suites and the OpenML100. arXiv e-prints (2017)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the 11th Annual Conference on Computational Learning Theory, pp. 92\u2013100. ACM (1998)","DOI":"10.1145\/279943.279962"},{"key":"21_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73263-1","volume-title":"Metalearning: Applications to Data Mining","author":"P Brazdil","year":"2009","unstructured":"Brazdil, P., Giraud-Carrier, C., Soares, C., Vilalta, R.: Metalearning: Applications to Data Mining. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-540-73263-1"},{"issue":"1","key":"21_CR5","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). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A., Crew, G., Ksikes, A.: Ensemble selection from libraries of models. In: Proceedings of the 21st International Conference on Machine Learning, pp. 18\u201325. ACM (2004)","DOI":"10.1145\/1015330.1015432"},{"key":"21_CR7","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1\u201315. Springer, Heidelberg (2000). https:\/\/doi.org\/10.1007\/3-540-45014-9_1"},{"issue":"55","key":"21_CR9","first-page":"1","volume":"20","author":"T Elsken","year":"2019","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. J. Mach. Learn. Res. 20(55), 1\u201321 (2019)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"21_CR10","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","volume":"109","author":"JE van Engelen","year":"2019","unstructured":"van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109(2), 373\u2013440 (2019). https:\/\/doi.org\/10.1007\/s10994-019-05855-6","journal-title":"Mach. Learn."},{"key":"21_CR11","unstructured":"Van Engelen, J.E.: Semi-supervised ensemble learning. Master\u2019s thesis, Leiden University, July 2018"},{"key":"21_CR12","unstructured":"Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., Hutter, F.: Efficient and robust automated machine learning. In: Advances in Neural Information Processing Systems, pp. 2962\u20132970 (2015)"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Guyon, I., et al.: Design of the 2015 ChaLearn AutoML challenge. In: IEEE International Joint Conference on Neural Networks, pp. 1\u20138. IEEE (2015)","DOI":"10.1109\/IJCNN.2015.7280767"},{"issue":"1","key":"21_CR14","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10\u201318 (2009)","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Komer, B., Bergstra, J., Eliasmith, C.: Hyperopt-Sklearn: automatic hyperparameter configuration for Scikit-learn. In: 31st ICML Workshop on AutoML (2014)","DOI":"10.25080\/Majora-14bd3278-006"},{"issue":"1","key":"21_CR16","first-page":"826","volume":"18","author":"L Kotthoff","year":"2017","unstructured":"Kotthoff, L., Thornton, C., Hoos, H.H., Hutter, F., Leyton-Brown, K.: Auto-WEKA 2.0: automatic model selection and hyperparameter optimization in WEKA. J. Mach. Learn. Res. 18(1), 826\u2013830 (2017)","journal-title":"J. Mach. Learn. Res."},{"issue":"6","key":"21_CR17","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TSMCA.2007.904745","volume":"37","author":"M Li","year":"2007","unstructured":"Li, M., Zhou, Z.H.: Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37(6), 1088\u20131098 (2007)","journal-title":"IEEE Trans. Syst. Man Cybern. Part A Syst. Hum."},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Li, Y.F., Wang, H., Wei, T., Tu, W.W.: Towards automated semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 4237\u20134244 (2019)","DOI":"10.1609\/aaai.v33i01.33014237"},{"issue":"1","key":"21_CR19","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1109\/TPAMI.2014.2299812","volume":"37","author":"YF Li","year":"2015","unstructured":"Li, Y.F., Zhou, Z.H.: Towards making unlabeled data never hurt. IEEE Trans. Pattern Anal. Mach. Intell. 37(1), 175\u2013188 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR20","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2019","unstructured":"Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41, 1979\u20131993 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR21","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR22","unstructured":"Rasmus, A., Berglund, M., Honkala, M., Valpola, H., Raiko, T.: Semi-supervised learning with ladder networks. In: Advances in Neural Information Processing Systems, pp. 3546\u20133554 (2015)"},{"key":"21_CR23","unstructured":"Singh, A., Nowak, R., Zhu, X.: Unlabeled data: now it helps, now it doesn\u2019t. In: Advances in Neural Information Processing Systems, pp. 1513\u20131520 (2009)"},{"key":"21_CR24","unstructured":"Snoek, J., Larochelle, H., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in Neural Information Processing Systems, pp. 2951\u20132959 (2012)"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Thornton, C., Hutter, F., Hoos, H.H., Leyton-Brown, K.: Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 847\u2013855. ACM (2013)","DOI":"10.1145\/2487575.2487629"},{"issue":"2","key":"21_CR26","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s10115-013-0706-y","volume":"42","author":"I Triguero","year":"2013","unstructured":"Triguero, I., Garc\u00eda, S., Herrera, F.: Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study. Knowl. Inf. Syst. 42(2), 245\u2013284 (2013). https:\/\/doi.org\/10.1007\/s10115-013-0706-y","journal-title":"Knowl. Inf. Syst."},{"issue":"6","key":"21_CR27","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2307\/3001968","volume":"1","author":"F Wilcoxon","year":"1945","unstructured":"Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80\u201383 (1945)","journal-title":"Biom. Bull."},{"issue":"11","key":"21_CR28","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1109\/TKDE.2005.186","volume":"17","author":"ZH Zhou","year":"2005","unstructured":"Zhou, Z.H., Li, M.: Tri-training: exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng. 17(11), 1529\u20131541 (2005)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"21_CR29","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1007\/s10115-009-0209-z","volume":"24","author":"ZH Zhou","year":"2010","unstructured":"Zhou, Z.H., Li, M.: Semi-supervised learning by disagreement. Knowl. Inf. Syst. 24(3), 415\u2013439 (2010). https:\/\/doi.org\/10.1007\/s10115-009-0209-z","journal-title":"Knowl. Inf. Syst."},{"key":"21_CR30","unstructured":"Zhu, X.: Semi-supervised learning literature survey. Technical report 1530, University of Wisconsin-Madison (2008)"}],"container-title":["Lecture Notes in Computer Science","Trustworthy AI - Integrating Learning, Optimization and Reasoning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-73959-1_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,12]],"date-time":"2021-04-12T15:34:50Z","timestamp":1618241690000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-73959-1_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030739584","9783030739591"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-73959-1_21","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":"13 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TAILOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on the Foundations of Trustworthy AI Integrating Learning, Optimization and Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Santiago de Compestela","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":"4 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tailor2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/tailor-network.eu\/activities\/tailor-workshop-2020\/","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":"52","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":"11","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":"6","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":"21% - 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":"3,5","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)"}}]}}