An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote
Metadata
Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7037
comunitat-uji-handle3:10234/8635
comunitat-uji-handle4:
INVESTIGACIONMetadata
Title
An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority VoteDate
2021-06-01Publisher
Multidisciplinary Digital Publishing InstituteISSN
2227-7390Bibliographic citation
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/math9121315Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/2227-7390/9/12/1315Version
info:eu-repo/semantics/publishedVersionSubject
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, ... [+]
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. [-]
Is part of
Mathematics 2021, 9(12), 1315Rights
info:eu-repo/semantics/openAccess
This item appears in the folowing collection(s)
- IMAC_Articles [122]
- MAT_Articles [765]