An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote
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Título
An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority VoteFecha de publicación
2021-06-01Editor
Multidisciplinary Digital Publishing InstituteISSN
2227-7390Cita bibliográfica
Ghanem, S.; Couturier, R.; Gregori, P. An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote. Mathematics 2021, 9, 1315. https:// doi.org/10.3390/math9121315Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://www.mdpi.com/2227-7390/9/12/1315Versión
info:eu-repo/semantics/publishedVersionPalabras clave / Materias
Resumen
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, ... [+]
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. [-]
Publicado en
Mathematics 2021, 9(12), 1315Derechos de acceso
info:eu-repo/semantics/openAccess
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- IMAC_Articles [122]
- MAT_Articles [766]