EOG-based drowsiness detection: Comparison between a fuzzy system and two supervised learning classifiers

2011 ◽  
Vol 44 (1) ◽  
pp. 14283-14288 ◽  
Author(s):  
Antoine Picot ◽  
Sylvie Charbonnier ◽  
Alice Caplier
Keyword(s):  
Supervised Learning ◽  
Fuzzy System ◽  
Drowsiness Detection ◽  
Learning Classifiers

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

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1315
Author(s):  
Souhila Ghanem ◽  
Raphaël Couturier ◽  
Pablo Gregori
Keyword(s):  
Neural Networks ◽  
Open Access ◽  
Supervised Learning ◽  
Association Rules ◽  
Majority Vote ◽  
Binary Classifier ◽  
Learning Classifiers

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.

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