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The Effects of Abrupt Changing Data in CART Inference Models

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Trends and Applications in Information Systems and Technologies (WorldCIST 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1366))

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Abstract

The continuous input of data into an Information System makes it difficult to generate a reliable model when this stream changes unpredictably. This continuous and unexpected change of data, known as concept drift, is faced by different strategies depending on its type. Several contributions are focused on the adaptations of traditional Machine Learning techniques to solve these data streams problems. The decision tree is one of the most used Machine Learning techniques due to its high interpretability. This article aims to study the impact an abrupt concept change has on the accuracy of the original CART proposed by Breiman, and justify the necessity of detection and/or adaptation methodologies that update or rebuild the model when a concept drift occurs. To do that, some simulated experiences have been carried out to study several training and testing conditions in a changing data environment. According to the results, models that are rebuilt in the right moment after a concept drift occurs obtain high accuracy rates while those that are not rebuilt or are rebuilt after a change occurs, obtain considerably lower accuracies.

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Acknowledgments

The authors are grateful for the financial support from Spanish Ministry of Science, Innovation and Universities under Grant FPU17/05365 and Grant DIN2018-010101. This work was also supported by the Spanish Ministry of Science and Innovation and the State Research Agency under grant PID2019-105952GB-I00/AEI/https://doi.org/10.13039/501100011033 and Miguel Hernández University under grant 197T/20.

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Authors and Affiliations

  1. Center of Operations Research (CIO), Miguel Hernandez University of Elche (UMH), 03202, Elche (Alicante), Spain

    Miriam Esteve, Nuria Mollá-Campello, Jesús J. Rodríguez-Sala & Alejandro Rabasa

Authors
  1. Miriam Esteve
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  2. Nuria Mollá-Campello
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  3. Jesús J. Rodríguez-Sala
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  4. Alejandro Rabasa
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Corresponding author

Correspondence to Miriam Esteve .

Editor information

Editors and Affiliations

  1. ISEG, University of Lisbon, Lisbon, Portugal

    Álvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. Department of Information Sciences, University of Sheffield, Lisbon, Portugal

    Ana Maria Ramalho Correia

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Esteve, M., Mollá-Campello, N., Rodríguez-Sala, J.J., Rabasa, A. (2021). The Effects of Abrupt Changing Data in CART Inference Models. In: Rocha, Á., Adeli, H., Dzemyda, G., Moreira, F., Ramalho Correia, A.M. (eds) Trends and Applications in Information Systems and Technologies . WorldCIST 2021. Advances in Intelligent Systems and Computing, vol 1366. Springer, Cham. https://doi.org/10.1007/978-3-030-72651-5_21

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