{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:22:59Z","timestamp":1774797779688,"version":"3.50.1"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030711573","type":"print"},{"value":"9783030711580","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-71158-0_14","type":"book-chapter","created":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T09:05:48Z","timestamp":1615626348000},"page":"291-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Scalable and Automated Machine Learning Framework to Support Risk Management"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4790-5128","authenticated-orcid":false,"given":"Lu\u00eds","family":"Ferreira","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4380-3220","authenticated-orcid":false,"given":"Andr\u00e9","family":"Pilastri","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0678-4868","authenticated-orcid":false,"given":"Carlos","family":"Martins","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4269-5838","authenticated-orcid":false,"given":"Pedro","family":"Santos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7991-2090","authenticated-orcid":false,"given":"Paulo","family":"Cortez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,14]]},"reference":[{"key":"14_CR1","unstructured":"Apache Spark: extracting, transforming and selecting features - Spark 2.4.5 documentation (2020) https:\/\/spark.apache.org\/docs\/latest\/ml-features"},{"key":"14_CR2","unstructured":"Apache Spark: ML pipelines - Spark 2.4.5 documentation (2020). https:\/\/spark.apache.org\/docs\/latest\/ml-pipeline.html"},{"key":"14_CR3","unstructured":"Auto-Gluon: AutoGluon: AutoML toolkit for deep learning \u2014 AutoGluon documentation 0.0.1 documentation (2020). https:\/\/autogluon.mxnet.io\/"},{"key":"14_CR4","doi-asserted-by":"publisher","unstructured":"Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artif. Intell. 97(1\u20132), 245\u2013271 (1997). https:\/\/doi.org\/10.1016\/s0004-3702(97)00063-5","DOI":"10.1016\/s0004-3702(97)00063-5"},{"key":"14_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"14_CR6","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/keras.io"},{"key":"14_CR7","unstructured":"Cook, D.: Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI. O\u2019Reilly Media, Inc., Sebastopol (2016)"},{"key":"14_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1007\/978-3-642-14400-4_44","volume-title":"Advances in Data Mining. Applications and Theoretical Aspects","author":"P Cortez","year":"2010","unstructured":"Cortez, P.: Data mining with neural networks and support vector machines using the R\/rminer tool. In: Perner, P. (ed.) ICDM 2010. LNCS (LNAI), vol. 6171, pp. 572\u2013583. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14400-4_44"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Cortez, P.: A tutorial on using the rminer r package for data mining tasks, Technical report, Universidade do Minho, Escola de Engenharia (EEng) (2015)","DOI":"10.21814\/1822.36210"},{"key":"14_CR10","unstructured":"Cortez, P.: Package \u2018rminer\u2019 (2020). https:\/\/cran.r-project.org\/web\/packages\/rminer\/rminer.pdf"},{"key":"14_CR11","doi-asserted-by":"publisher","unstructured":"Darwiche, A.: Human-level intelligence or animal-like abilities? Commun. ACM 61(10), 56\u201367 (2018). https:\/\/doi.org\/10.1145\/3271625","DOI":"10.1145\/3271625"},{"key":"14_CR12","unstructured":"Elshawi, R., Maher, M., Sakr, S.: Automated machine learning: state-of-the-art and open challenges. arXiv preprint arXiv:1906.02287 (2019)"},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey. arXiv preprint arXiv:1808.05377 (2018)","DOI":"10.1007\/978-3-030-05318-5_11"},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Ferreira, L., Pilastri, A., Martins, C., Santos, P., Cortez, P.: An automated and distributed machine learning framework for telecommunications risk management. In: Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, pp. 99\u2013107. INSTICC, SciTePress (2020). https:\/\/doi.org\/10.5220\/0008952800990107","DOI":"10.5220\/0008952800990107"},{"key":"14_CR15","unstructured":"Feurer, M., et al.: Efficient and robust automated machine learning. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 7\u201312 December 2015, Montreal, Quebec, Canada, pp. 2962\u20132970 (2015). http:\/\/papers.nips.cc\/paper\/5872-efficient-and-robust-automated-machine-learning"},{"key":"14_CR16","unstructured":"Feurer, M., Springenberg, J.T., Hutter, F.: Initializing Bayesian hyperparameter optimization via meta-learning. In: Bonet, B., Koenig, S. (eds.) Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25\u201330 January 2015, Austin, Texas, USA, pp. 1128\u20131135. AAAI Press (2015). http:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI15\/paper\/view\/10029"},{"key":"14_CR17","unstructured":"Gijsbers, P., LeDell, E., Thomas, J., Poirier, S., Bischl, B., Vanschoren, J.: An open source autoML benchmark. arXiv preprint arXiv:1907.00909 (2019)"},{"key":"14_CR18","doi-asserted-by":"publisher","unstructured":"Guyon, I., et al.: Design of the 2015 chalearn autoML challenge. In: 2015 International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, 12\u201317 July 2015, pp. 1\u20138. IEEE (2015). https:\/\/doi.org\/10.1109\/IJCNN.2015.7280767","DOI":"10.1109\/IJCNN.2015.7280767"},{"key":"14_CR19","unstructured":"Guyon, I., et al.: A brief review of the chalearn automl challenge: any-time any-dataset learning without human intervention. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Proceedings of the 2016 Workshop on Automatic Machine Learning, AutoML 2016, co-located with 33rd International Conference on Machine Learning (ICML 2016), New York City, NY, USA, 24 June 2016. JMLR Workshop and Conference Proceedings, vol. 64, pp. 21\u201330. JMLR.org (2016)"},{"key":"14_CR20","series-title":"The Springer Series on Challenges in Machine Learning","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-030-05318-5_10","volume-title":"Automated Machine Learning","author":"I Guyon","year":"2019","unstructured":"Guyon, I., et al.: Analysis of the AutoML challenge series 2015\u20132018. In: Hutter, F., Kotthoff, L., Vanschoren, J. (eds.) Automated Machine Learning. TSSCML, pp. 177\u2013219. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-05318-5_10"},{"key":"14_CR21","unstructured":"H2O.ai: H2O AutoML, June 2017. http:\/\/docs.h2o.ai\/h2o\/latest-stable\/h2o-docs\/automl.html, h2O version 3.30.0.1"},{"key":"14_CR22","unstructured":"He, X., Zhao, K., Chu, X.: AutoML: a survey of the state-of-the-art. arXiv preprint arXiv:1908.00709 (2019)"},{"key":"14_CR23","doi-asserted-by":"publisher","unstructured":"Jin, H., Song, Q., Hu, X.: Auto-keras: an efficient neural architecture search system. In: Teredesai, A., Kumar, V., Li, Y., Rosales, R., Terzi, E., Karypis, G. (eds.) Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, 4\u20138 August 2019, pp. 1946\u20131956. ACM (2019). https:\/\/doi.org\/10.1145\/3292500.3330648","DOI":"10.1145\/3292500.3330648"},{"key":"14_CR24","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, 25:1\u201325:5 (2017). http:\/\/jmlr.org\/papers\/v18\/16-261.html"},{"issue":"1","key":"14_CR25","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1093\/bioinformatics\/btz470","volume":"36","author":"TT Le","year":"2020","unstructured":"Le, T.T., Fu, W., Moore, J.H.: Scaling tree-based automated machine learning to biomedical big data with a feature set selector. Bioinformatics 36(1), 250\u2013256 (2020)","journal-title":"Bioinformatics"},{"key":"14_CR26","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.eswa.2016.12.036","volume":"73","author":"N Oliveira","year":"2017","unstructured":"Oliveira, N., Cortez, P., Areal, N.: The impact of microblogging data for stock market prediction: Using twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Syst. Appl. 73, 125\u2013144 (2017). https:\/\/doi.org\/10.1016\/j.eswa.2016.12.036","journal-title":"Expert Syst. Appl."},{"key":"14_CR27","doi-asserted-by":"publisher","unstructured":"Olson, R.S., Urbanowicz, R.J., Andrews, P.C., Lavender, N.A., Kidd, L.C., Moore,J.H.: Automating biomedical data science through tree-based pipeline optimization. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 123\u2013137. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-31204-0_9","DOI":"10.1007\/978-3-319-31204-0_9"},{"key":"14_CR28","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011). http:\/\/dl.acm.org\/citation.cfm?id=2078195"},{"issue":"1","key":"14_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13748-012-0035-5","volume":"2","author":"D Peteiro-Barral","year":"2013","unstructured":"Peteiro-Barral, D., Guijarro-Berdi\u00f1as, B.: A survey of methods for distributed machine learning. Prog. Artif. Intell. 2(1), 1\u201311 (2013). https:\/\/doi.org\/10.1007\/s13748-012-0035-5","journal-title":"Prog. Artif. Intell."},{"key":"14_CR30","unstructured":"Salesforce: Transmogrifai (2019). https:\/\/docs.transmogrif.ai\/en\/stable\/"},{"key":"14_CR31","doi-asserted-by":"publisher","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 (2013). https:\/\/doi.org\/10.1145\/2487575.2487629","DOI":"10.1145\/2487575.2487629"},{"key":"14_CR32","doi-asserted-by":"crossref","unstructured":"Truong, A., Walters, A., Goodsitt, J., Hines, K., Bruss, B., Farivar, R.: Towards automated machine learning: Evaluation and comparison of autoML approaches and tools. arXiv preprint arXiv:1908.05557 (2019)","DOI":"10.1109\/ICTAI.2019.00209"},{"issue":"2","key":"14_CR33","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/2641190.2641198","volume":"15","author":"J Vanschoren","year":"2013","unstructured":"Vanschoren, J., van Rijn, J.N., Bischl, B., Torgo, L.: OpenML: networked science in machine learning. SIGKDD Explor. 15(2), 49\u201360 (2013). https:\/\/doi.org\/10.1145\/2641190.2641198","journal-title":"SIGKDD Explor."},{"key":"14_CR34","unstructured":"Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam (2016)"},{"key":"14_CR35","unstructured":"Yao, Q., et al.: Taking human out of learning applications: a survey on automated machine learning. arXiv preprint arXiv:1810.13306 (2018)"},{"key":"14_CR36","unstructured":"Z\u00f6ller, M.A., Huber, M.F.: Benchmark and survey of automated machine learning frameworks. Technical report. https:\/\/www.researchgate.net\/publication\/332750780"}],"container-title":["Lecture Notes in Computer Science","Agents and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71158-0_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T09:17:55Z","timestamp":1615627075000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-71158-0_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030711573","9783030711580"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71158-0_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Agents and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valletta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malta","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":"22 February 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 February 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icaart2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.icaart.org\/?y=2020","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":"PRIMORIS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"276","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":"45","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":"74","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":"16% - 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)"}}]}}