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Sammon Mapping-Based Gradient Boosted Trees for Tax Crime Prediction in the City of São Paulo

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Enterprise Information Systems (ICEIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 417))

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Abstract

With the currently vast volume of data available, several institutions, including the public sector, benefit from information, aiming to improve decision-making. Machine Learning enhances data-driven decision-making with its predictive power. In this work, our principal motivation was to apply Machine Learning to ameliorate fiscal audit planning for São Paulo’s municipality. In this study, we predicted crimes against the service tax system of São Paulo using Machine Learning. Our methodology embraced the following steps: data extraction; data preparation; dimensionality reduction; model training and testing; model evaluation; model selection. Our experimental findings revealed that Sammon Mapping (SM) combined with Gradient Boosted Trees (GBT) outranked other state-of-the-art works, classifiers and dimensionality reduction techniques as regards classification performance. Our belief is that the ensemble of classifiers of GBT, combined with SM’s ability to identify relevant dimensions in data, contributed to produce higher prediction scores. These scores enable São Paulo’s tax administration to rank fiscal audits according to the highest probabilities of tax crime occurrence, leveraging tax revenue.

Supported by São Paulo City Hall.

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Author information

Authors and Affiliations

  1. Tax Intelligence Office, Under-secretariat of Municipal Revenue, Secretariat of Finance, São Paulo City Hall, São Paulo, Brazil

    André Ippolito & Augusto Cezar Garcia Lozano

Authors
  1. André Ippolito
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  2. Augusto Cezar Garcia Lozano
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Corresponding author

Correspondence to André Ippolito .

Editor information

Editors and Affiliations

  1. Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

  2. Warsaw University of Technology, Warsaw, Poland

    Michał Śmiałek

  3. George Mason University, Fairfax, VA, USA

    Alexander Brodsky

  4. MODESTE/ESEO, Angers, France

    Slimane Hammoudi

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Ippolito, A., Lozano, A.C.G. (2021). Sammon Mapping-Based Gradient Boosted Trees for Tax Crime Prediction in the City of São Paulo. In: Filipe, J., Śmiałek, M., Brodsky, A., Hammoudi, S. (eds) Enterprise Information Systems. ICEIS 2020. Lecture Notes in Business Information Processing, vol 417. Springer, Cham. https://doi.org/10.1007/978-3-030-75418-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-75418-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75417-4

  • Online ISBN: 978-3-030-75418-1

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