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Imbalanced learning in assessing the risk of corruption in public administration

2021Conference object Open access
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dc.contributor.authorVasconcelos, Marcelo Oliveira
dc.contributor.authorChaim, Ricardo Matos
dc.contributor.authorCavique, Luís
dc.date.accessioned2021-12-22T16:47:17Z
dc.date.available2021-12-22T16:47:17Z
dc.date.issued2021
dc.description.abstractThis research aims to identify the corruption of the civil servants in the Federal District, Brazilian Public Administration. For this purpose, a predictive model was created integrating data from eight different systems and applying logistic regression to real datasets that, by their nature, present a low percentage of examples of interest in identifying patterns for machine learning, a situation defined as a class imbalance. In this study, the imbalance of classes was considered extreme at a ratio of 1:707 or, in percentage terms, 0.14% of the interest class to the population. Two possible approaches were used, balancing with resampling techniques using synthetic minority oversampling technique SMOTE and applying algorithms with specific parameterization to obtain the desired standards of the minority class without generating bias from the dominant class. The best modeling result was obtained by applying it to the second approach, generating an area value on the ROC curve of around 0.69. Based on sixty-eight features, the respective coefficients that correspond to the risk factors for corruption were found. A subset of twenty features is discussed in order to find practical utility after the discovery process.pt_PT
dc.description.sponsorships. L.Cavique would like to thank the FCT Projects of Scientific Research and Technological Development in Data Science and Artificial Intelligence in Public Administration, 2018–2022 (DSAIPA/DS/0039/2018), for its support.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-86230-5_40pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.2/11541
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectData enrichmentpt_PT
dc.subjectImbalanced learningpt_PT
dc.subjectCorruptionpt_PT
dc.subjectPublic administrationpt_PT
dc.titleImbalanced learning in assessing the risk of corruption in public administrationpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0039%2F2018/PT
oaire.citation.endPage523pt_PT
oaire.citation.startPage510pt_PT
oaire.citation.titleEPIA 2021. Conference on Artificial Intelligencept_PT
oaire.citation.volume12981pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameOliveira Vasconcelos
person.familyNameCHAIM
person.familyNameCavique
person.givenNameMarcelo
person.givenNameRICARDO MATOS
person.givenNameLuís
person.identifier.ciencia-id911E-84AC-3956
person.identifier.orcid0000-0002-8563-1404
person.identifier.orcid0000-0003-0206-7076
person.identifier.orcid0000-0002-5590-1493
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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