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European Conference on Advances in Databases and Information Systems

ADBIS 2022: New Trends in Database and Information Systems pp 299–308Cite as

QLDT: A Decision Tree Based on Quantum Logic

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1652))

Abstract

Besides a good prediction a classifier is to give an explanation how input data is related to the classification result. Decision trees are very popular classifiers and provide a good trade-off between accuracy and explainability for many scenarios. Its split decisions correspond to Boolean conditions on single attributes. In cases when for a class decision several attribute values interact gradually with each other, Boolean-logic-based decision trees are not appropriate. For such cases we propose a quantum-logic inspired decision tree (QLDT) which is based on sums and products on normalized attribute values. In contrast to decision trees based on fuzzy logic a QLDT obeys the rules of the Boolean algebra.

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Notes

  1. 1.

    Of course, there exist classification problems for which the decision tree based on Boolean logic fits perfectly.

  2. 2.

    https://archive.ics.uci.edu/ml/datasets.php.

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Author information

Authors and Affiliations

  1. Brandenburgische Technische Universität Cottbus-Senftenberg, Cottbus, Germany

    Ingo Schmitt

Authors
  1. Ingo Schmitt
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Correspondence to Ingo Schmitt .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

  4. Norwegian University of Science and Technology, Trondheim, Norway

    Kjetil Nørvåg

  5. University of Genoa, Genoa, Italy

    Barbara Catania

  6. CNRS, Villeurbanne Cedex, France

    Genoveva Vargas-Solar

  7. University of Calabria, Rende, Italy

    Ester Zumpano

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Cite this paper

Schmitt, I. (2022). QLDT: A Decision Tree Based on Quantum Logic. In: Chiusano, S., et al. New Trends in Database and Information Systems. ADBIS 2022. Communications in Computer and Information Science, vol 1652. Springer, Cham. https://doi.org/10.1007/978-3-031-15743-1_28

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  • DOI: https://doi.org/10.1007/978-3-031-15743-1_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15742-4

  • Online ISBN: 978-3-031-15743-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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