Authors:
Yúri Faro Dantas de Sant’Anna
1
;
Mariana Lira de Farias
2
;
3
;
Methanias Colaço Júnior
3
;
2
;
Daniel Dantas
2
;
3
and
Max Rodrigues Junior
3
Affiliations:
1
Centro de Informática, Universidade Federal de Pernambuco, Recife, PE, Brazil
;
2
Departamento de Computação, Universidade Federal de Sergipe, São Cristóvão, SE, Brazil
;
3
Centro Universitário Estácio de Sergipe, Aracaju, SE, Brazil
Keyword(s):
Supervised Learning, Invoice, Text Classification, Naive Bayes.
Abstract:
The Tax on the Circulation of Goods and Services (Imposto sobre Circulação de Mercadorias e Serviços, ICMS), a responsibility of the federative units, is the main Brazilian tax collection resource. One way to collect this tax is through a product’s weighted average price to the end consumer (preço médio ponderado ao consumidor final, PMPF) of a product. The PMPF is the only resource for charging state fees for the fuel segment, so if improperly calculated, it can lead to losses both in the collection of public funds and in the evolution of prices practiced by merchants. The objective of this work is to make a comparative analysis of classification algorithms used to calculate the PMPF of fuels in the state of Sergipe to select the most appropriate technique. This system circumvented deficiencies present in the previously applied simple random sampling methodology. The naive Bayes algorithm was considered the most effective approach due to its high accuracy and feasibility of applicat
ion in a real-life scenario.
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