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A Game Theoretic Flavoured Decision Tree for Classification

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Machine Learning, Optimization, and Data Science (LOD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13811))

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Abstract

A game theoretic flavoured decision tree is designed for multi-class classification. Node data is split by using a game between sub-nodes that try to minimize their entropy. The splitting parameter is approximated by a naive approach that explores the deviations of players that can improve payoffs by unilateral deviations in order to imitate the behavior of the Nash equilibrium of the game. The potential of the approach is illustrated by comparing its performance with other decision tree-based approaches on a set of synthetic data.

This work was supported by a grant of the Romanian Ministry of Education and Research, CNCS - UEFISCDI, project number PN-III-P4-ID-PCE-2020-2360, within PNCDI III.

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Notes

  1. 1.

    UCI Machine Learning Repository https://archive.ics.uci.edu/ml/index.php, accessed March 2022.

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Authors and Affiliations

  1. Center for the Study of Complexity, Babeş-Bolyai University, Cluj Napoca, Romania

    Mihai-Alexandru Suciu & Rodica-Ioana Lung

Authors
  1. Mihai-Alexandru Suciu
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  2. Rodica-Ioana Lung
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Corresponding author

Correspondence to Rodica-Ioana Lung .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Reading, Reading, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  6. Free University of Bozen-Bolzano, Bolzano, Italy

    Giuseppe Di Fatta

  7. University of Catania, Catania, Italy

    Giovanni Giuffrida

  8. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Suciu, MA., Lung, RI. (2023). A Game Theoretic Flavoured Decision Tree for Classification. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13811. Springer, Cham. https://doi.org/10.1007/978-3-031-25891-6_2

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  • DOI: https://doi.org/10.1007/978-3-031-25891-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25890-9

  • Online ISBN: 978-3-031-25891-6

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