{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T02:26:56Z","timestamp":1778034416299,"version":"3.51.4"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030862299","type":"print"},{"value":"9783030862305","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":"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\/978-3-030-86230-5_40","type":"book-chapter","created":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:03:00Z","timestamp":1631005380000},"page":"510-523","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Imbalanced Learning in Assessing the Risk of Corruption in Public Administration"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8563-1404","authenticated-orcid":false,"given":"Marcelo Oliveira","family":"Vasconcelos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0206-7076","authenticated-orcid":false,"given":"Ricardo Matos","family":"Chaim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5590-1493","authenticated-orcid":false,"given":"Lu\u00eds","family":"Cavique","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,3]]},"reference":[{"key":"40_CR1","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fernandez","year":"2018","unstructured":"Fernandez, A., Garcia, S., Herrera, F., Chawla, N.V.: SMOTE for learning from imbalanced data: progress and challenges, marking the 15th anniversary. J. Artif. Intell. Res. 61, 863\u2013905 (2018)","journal-title":"J. Artif. Intell. Res."},{"key":"40_CR2","unstructured":"Brasil: Lei no 8429, de 2 de julho de 1992, DOU (1992). http:\/\/www.planalto.gov.br\/ccivil_03\/leis\/l8429.htm"},{"key":"40_CR3","unstructured":"Brownlee, J.: Imbalanced Classification with Python Choose Better Metrics, Balance Skewed Classes, and Apply Cost-Sensitive Learning. Machine Learning Mastery, vol. V1.2, pp. 1\u201322 (2020)"},{"issue":"1","key":"40_CR4","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(1), 321\u2013357 (2002). https:\/\/doi.org\/10.1613\/jair.953","journal-title":"J. Artif. Intell. Res."},{"key":"40_CR5","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Kaufmann, M., (ed.), 3 (2012)"},{"key":"40_CR6","unstructured":"Hosmer, D., Lemeshow, S.: Applied Survival Analysis - Regression Modeling of Time to Event Data, John Wiley, New York, pp. 386 (1999)"},{"key":"40_CR7","doi-asserted-by":"publisher","unstructured":"Knapp, E.D., Langill, J.T. Industrial network security: securing critical infrastructure networks for smart grid, SCADA, and other industrial control systems. In: Industrial Network Security: Securing Critical Infrastructure Networks for Smart Grid, SCADA, and Other Industrial Control Systems, Second Edition (2014). https:\/\/doi.org\/10.1016\/B978-0-12-420114-9.00018-6","DOI":"10.1016\/B978-0-12-420114-9.00018-6"},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.knosys.2014.01.012","volume":"59","author":"M Maalouf","year":"2014","unstructured":"Maalouf, M., Siddiqi, M.: Weighted logistic regression for large-scale imbalanced and rare events data. Knowl.-Based Syst. 59, 142\u2013148 (2014). https:\/\/doi.org\/10.1016\/j.knosys.2014.01.012","journal-title":"Knowl.-Based Syst."},{"issue":"1","key":"40_CR9","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1016\/j.csda.2010.06.014","volume":"55","author":"M Maalouf","year":"2011","unstructured":"Maalouf, M., Trafalis, T.B.: Robust weighted kernel logistic regression in imbalanced and rare events data. Comput. Stat. Data Anal. 55(1), 168\u2013183 (2011). https:\/\/doi.org\/10.1016\/j.csda.2010.06.014","journal-title":"Comput. Stat. Data Anal."},{"issue":"9","key":"40_CR10","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1097\/JTO.0b013e3181ec173d","volume":"5","author":"JN Mandrekar","year":"2010","unstructured":"Mandrekar, J.N.: Receiver operating characteristic curve in diagnostic test assessment. J. Thorac. Oncol. 5(9), 1315\u20131316 (2010). https:\/\/doi.org\/10.1097\/JTO.0b013e3181ec173d","journal-title":"J. Thorac. Oncol."},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Mauro, P.: Corruption and growth. Source: Q. J. Econ. 110(3), 681\u2013712 (1995)","DOI":"10.2307\/2946696"},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Olken, B.A.: Monitoring corruption\u202f: evidence from a field experiment in Indonesia. J. Polit. Econ. 115(2), 200\u2013249 (2007)","DOI":"10.1086\/517935"},{"issue":"4","key":"40_CR13","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1590\/S0034-759020180406","volume":"58","author":"AJA Padula","year":"2018","unstructured":"Padula, A.J.A., Albuquerque, P.H.M.: Government corruption on Brazilian capital markets: a study on Lava Jato (Car Wash) investigation. Revista de Administra\u00e7\u00e3o de Empresas 58(4), 405\u2013417 (2018). https:\/\/doi.org\/10.1590\/S0034-759020180406","journal-title":"Revista de Administra\u00e7\u00e3o de Empresas"},{"key":"40_CR14","doi-asserted-by":"publisher","unstructured":"Szumilas, M.: Explaining odds ratios. J. Can. Acad. Child. Adolesc. Psychiatry, 341(19:3), 227\u2013229 (2010). https:\/\/doi.org\/10.1136\/bmj.c4414","DOI":"10.1136\/bmj.c4414"},{"key":"40_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/978-3-642-40669-0_33","volume-title":"Progress in Artificial Intelligence","author":"L Torgo","year":"2013","unstructured":"Torgo, L., Ribeiro, R.P., Pfahringer, B., Branco, P.: SMOTE for regression. In: Correia, L., Reis, L.P., Cascalho, J. (eds.) EPIA 2013. LNCS (LNAI), vol. 8154, pp. 378\u2013389. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40669-0_33"},{"key":"40_CR16","unstructured":"Transparency International. (n.d.).: Transparency International - What is Corruption?, 16 June 2019. https:\/\/www.transparency.org\/what-is-corruption"},{"key":"40_CR17","unstructured":"Vimalraj, S., Rajendran, P.: A review on handling imbalanced data. In: International Conference on Current Trends towards Converging Technologies (ICCTCT), pp. 1\u201311. IEEE (2018)"},{"issue":"1","key":"40_CR18","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1057\/s41274-016-0176-1","volume":"69","author":"B Zhu","year":"2018","unstructured":"Zhu, B., Baesens, B., Backiel, A., Vanden Broucke, S.K.L.M.: Benchmarking sampling techniques for imbalance learning in churn prediction. J. Oper. Res. Soc. 69(1), 49\u201365 (2018). https:\/\/doi.org\/10.1057\/s41274-016-0176-1","journal-title":"J. Oper. Res. Soc."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86230-5_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T09:13:51Z","timestamp":1631006031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86230-5_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030862299","9783030862305"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86230-5_40","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":"3 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.appia.pt\/epia2021\/","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":"108","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":"62","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":"57% - 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":"3.47","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":"1.36","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)"}}]}}