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An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

Author

Listed:
  • Souhila Ghanem

    (Laboratoire LIMED, Faculty of Science Exact, Université de Bejaia, Bejaia 06000, Algeria)

  • Raphaël Couturier

    (FEMTO-ST Institute, CNRS UMR 6174, Université Bourgogne Franche-Comte, 90000 Belfort, France)

  • Pablo Gregori

    (Instituto Universitario de Matemáticas y Aplicaciones de Castellón, Universitat Jaume I de Castellón, E-12071 Castellón de la Plana, Spain)

Abstract

In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to more complex, less interpretable and more accurate ones (e.g., neural networks, SVM). In this tradeoff between interpretability and accuracy, we propose a new classifier based on association rules, that is to say, both easy to interpret and leading to relevant accuracy. To illustrate this proposal, its performance is compared to other widely used methods on six open access datasets.

Suggested Citation

  • Souhila Ghanem & Raphaël Couturier & Pablo Gregori, 2021. "An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote," Mathematics, MDPI, vol. 9(12), pages 1-12, June.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:12:p:1315-:d:570740
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    References listed on IDEAS

    as
    1. Armand & André Totohasina & Daniel Rajaonasy Feno, 2019. "An Extension of Totohasina’s Normalization Theory of Quality Measures of Association Rules," International Journal of Mathematics and Mathematical Sciences, Hindawi, vol. 2019, pages 1-7, January.
    2. Stéphane Lallich & Benoît Vaillant & Philippe Lenca, 2007. "A Probabilistic Framework Towards the Parameterization of Association Rule Interestingness Measures," Methodology and Computing in Applied Probability, Springer, vol. 9(3), pages 447-463, September.
    3. Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, vol. 24(2), pages 225-232, May.
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