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Authors: Helton Souza Lima 1 ; Damires Yluska de Souza Fernandes 1 ; Thiago José Marques Moura 1 and Daniel Sabóia 2

Affiliations: 1 Instituto Federal da Paraíba, 720 Avenida Primeiro de Maio, João Pessoa, Brazil ; 2 Procuradoria-Geral da Fazenda Nacional, Esplanada dos Ministérios Bloco P, Brasília, Brazil

Keyword(s): Government Data, Tax, Supervised Learning, Imbalanced Data.

Abstract: Tax management is a complex problem faced by governments around the world. In Brazil, in order to help solving problems in this area, data analytics has been increasingly used to support and enhance tax management processes. In this light, this work proposes an approach which uses supervised learning in order to classify requests of an administrative service. The requests at hand are named as Requests for Revision of Registered Debt (R3Ds). The service underlying such requests is offered by the Brazil’s National Treasury Attorney-General's Office and usually deals with a high volume of registrations. The experimental evaluation accomplished in this work presents some promising results. The obtained classification models present good levels of accuracy, area under ROC curve and recall. Four evaluation scenarios have been experimented, including imbalanced and balanced data. The Random Forest model achieves the best results in all the evaluated scenarios.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Lima, H.; Fernandes, D.; Moura, T. and Sabóia, D. (2021). On the Evaluation of Classification Methods Applied to Requests for Revision of Registered Debts. In Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-509-8; ISSN 2184-4992, SciTePress, pages 335-342. DOI: 10.5220/0010498403350342

@conference{iceis21,
author={Helton Souza Lima. and Damires Yluska de Souza Fernandes. and Thiago José Marques Moura. and Daniel Sabóia.},
title={On the Evaluation of Classification Methods Applied to Requests for Revision of Registered Debts},
booktitle={Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2021},
pages={335-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010498403350342},
isbn={978-989-758-509-8},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 23rd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - On the Evaluation of Classification Methods Applied to Requests for Revision of Registered Debts
SN - 978-989-758-509-8
IS - 2184-4992
AU - Lima, H.
AU - Fernandes, D.
AU - Moura, T.
AU - Sabóia, D.
PY - 2021
SP - 335
EP - 342
DO - 10.5220/0010498403350342
PB - SciTePress

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