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

Souhila Ghanem, Raphaël Couturier and Pablo Gregori
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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

Mathematics, 2021, vol. 9, issue 12, 1-12

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.

Keywords: classification; association rules; open access datasets; statistical implicative analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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