Clasificación de la percepción de los servidores públicos federales en relación a actos de corrupción utilizando algoritmos de aprendizaje de máquina

Adam, I. & Fazekas, M. (2021). Are emerging technologies helping win the fight against corruption? A review of the state of evidence. Information Economics and Policy, 57. https://doi.org/10.1016/j.infoecopol.2021.100950

Aggarwal, C. C. (2015). Data mining: the textbook (Vol. 1). New York: springer.

Breiman, L. (2001). Random forests. Machine learning, 45. https://doi.org/10.1023/A:1010933404324

Ashforth, B. E., Gioia, D. A., Robinson, S. L., & Trevino, L. K. (2008). Re-viewing organizational corruption. Academy of Management review, 33(3). https://doi.org/10.5465/amr.2008.32465714

Castro, L. N. D., & Ferrari, D. G. (2016). Introdução à mineração de dados: conceitos básicos, algoritmos e aplicações. São Paulo: Saraiva, 5.

Chen, S. H. & Liao, C. C. (2011). Are foreign banks more profitable than domestic banks? Home-and host-country effects of banking market structure, governance, and supervision. Journal of Banking & Finance, 35(4). https://doi.org/10.1016/j.jbusres.2022.03.032

Fawcett, T. (2006). An introduction to ROC analysis. Pattern recognition letters, 27(8). https://doi.org/10.1016/j.patrec.2005.10.010

Fernandes, A. A. T., Figueiredo Filho, D. B., Rocha, E. C. D., & Nascimento, W. D. S. (2021). Leia este artigo se você quiser aprender regressão logística. Revista de Sociologia e Política, 28. https://doi.org/10.1590/1678-987320287406en

Gonzalez, L. D. A. (2018). Regressão logística e suas aplicações. Recuperado de https://monografias.ufma.br/jspui/bitstream/123456789/3572/1/LEANDRO-GONZALEZ.pdf

de Blasio, G., D'Ignazio, A., & Letta, M. (2022). Gotham city. Predicting ‘corrupted’ municipalities with machine learning. Technological Forecasting and Social Change, 184. https://doi.org/10.1016/j.techfore.2022.122016

Domashova, J. & Politova, A. (2021). The Corruption Perception Index: analysis of dependence on socio-economic indicators. Procedia Computer Science, 190. https://doi.org/10.1016/j.procs.2021.06.024

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3). https://doi.org/10.1609/aimag.v17i3.1230

Gamarra, C., Guerrero, J. M., & Montero, E. (2016). A knowledge discovery in databases approach for industrial microgrid planning. Renewable and Sustainable Energy Reviews, 60. https://doi.org/10.1016/j.rser.2016.01.091

Gehrke, G., Borba, J. A., & Ferreira, D. D. M. (2017). A repercussão da corrupção brasileira na mídia: uma análise comparada das revistas Der Spiegel, L ‘Obs, The Economist, Time e Veja. Revista de Administração Pública, 5. http://dx.doi.org/10.1590/0034-7612158681

Géron, A. (2019). Mãos à Obra: Aprendizado de Máquina com Scikit-Learn & TensorFlow. Alta Books.

Goutte, S., Péran, T., & Porcher, T. (2022). Corruption, economy and governance in Central Africa: An analysis of public and regional drivers of corruption. Finance Research Letters, 44. https://dx.doi.org/10.2139/ssrn.3808716

Jackson, D. & Köbis, N. (2018). Anti-corruption through a social norms lens. U4 Issue, 7. Recuperado de https://www.u4.no/publications/anti-corruption-through-a-social-norms-lens#conclusion-a-social-norms-approach-to-anti-corruption

James, G., Witten, D., Hastie, T., Tibshirani, R., & Taylor, J. (2023). An Introduction to Statistical Learning with Applications in Python. Springer.

Jancsics, D. (2019). Corruption as resource transfer: An interdisciplinary synthesis. Public Administration Review, 79(4). https://doi.org/10.1111/puar.13024

Jeong, D., Shenoy, A., & Zimmermann, L. V. (2023). De Jure versus De Facto transparency: Corruption in local public office in India. Journal of Public Economics, 221. https://doi.org/10.1016/j.jpubeco.2023.104855

Li, J., Chen, W. H., Xu, Q., Shah, N., Kohler, J. C., & Mackey, T. K. (2020). Detection of self-reported experiences with corruption on twitter using unsupervised machine learning. Social Sciences & Humanities Open, 2(1). https://doi.org/10.1016/j.ssaho.2020.100060

Lima, M. S. M. & Delen, D. (2020). Predicting and explaining corruption across countries: A machine learning approach. Government Information Quarterly, 37(1). https://doi.org/10.1016/j.giq.2019.101407

Lino, A. F., Azevedo, R. R., de., Aquino, A. C. B., de., & Steccolini, I. (2022). Fighting or supporting corruption? The role of public sector audit organizations in Brazil. Critical Perspectives on Accounting, 83. https://doi.org/10.1016/j.cpa.2021.102384

Macedo, S. V. & Valadares, J. L. (2021). Corrupção: reflexões epistemológicas e contribuições para o campo de públicas. Organizações & Sociedade, 28. https://doi.org/10.1590/1984-92302021v28n9607PT

Mitchell, T. M. (1997). Machine learning. (Vol. 1). New York: McGraw-hill.

Pan, Z., Wang, Y., & Pan, Y. (2020). A new locally adaptive k-nearest neighbor algorithm based on discrimination class. Knowledge-Based Systems, 204.

https://doi.org/10.1016/j.knosys.2020.106185

Raschka, S. (2015). Python machine learning. Packt publishing ltd.

Speiser, J. L., Miller, M. E., Tooze, J., & Ip, E. (2019). A comparison of random forest variable selection methods for classification prediction modeling. Expert systems with applications, 134. https://doi.org/10.1016/j.eswa.2019.05.028

Transparência Internacional. (2022). Índice de Percepção da Corrupção 2022. Recuperado de https://www.transparency.org/en/cpi/2022/index/brasil