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English Information

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Authors
# Name
1 Cleyton Pires(cleyton07@gmail.com)

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Reference
# Reference
1 Chen, T. and Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, pages 785–794, New York, NY, USA. ACM.
2 da Silva L. S., de C., R. H., N., C. R., and F, S. J. C. (2016). Bayesian networks on income tax audit selection —a case study of brazilian tax administration. In Bayesian Modeling Application Workshop (BMAW).
3 de Roux, D., Perez, B., Moreno, A., Villamil, M. D. P., and Figueroa, F. (2018). Tax fraud detection for under-reporting declarations using an unsupervised machine learning approach. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ‘18), pages 215–222.
4 Lin, Y. et al. (2021). Taxthemis: Interactive mining and exploration of suspicious tax evasion groups. IEEE Transactions on Visualization & Computer Graphics, 27(02):849–859.
5 Lundberg, S. M. and Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Guyon, I., Luxburg, U. V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R., editors, Advances in Neural Information Processing Systems 30, pages 4765–4774. Curran Associates, Inc.
6 Savic, M. et al. (2021). Tax evasion risk management using a hybrid unsupervised outlier detection method. https://arxiv.org/pdf/2103.01033.pdf.
7 Wirth, R. and Hipp, J. (2000). Crisp-dm: Towards a standard process model for data mining. pages 29–39.
8 Xavier, O. et al. (2022). Tax evasion identification using open data and artificial intelligence. Revista de Administração Publica , 56:426–440.
9 Zumaya, M. et al. (2021). Identifying Tax Evasion in Mexico with Tools from Network Science and Machine Learning. Springer.