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Bayes classifiers for imbalanced traffic accidents datasets

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Bayes classifiers for imbalanced traffic accidents datasets

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Mujalli, R.; López-Maldonado, G.; Garach, L. (2016). Bayes classifiers for imbalanced traffic accidents datasets. Accident Analysis & Prevention. 88:37-51. https://doi.org/10.1016/j.aap.2015.12.003

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/120548

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Title: Bayes classifiers for imbalanced traffic accidents datasets
Author: Mujalli, R López-Maldonado, Griselda Garach, L.
UPV Unit: Universitat Politècnica de València. Departamento de Ingeniería e Infraestructura de los Transportes - Departament d'Enginyeria i Infraestructura dels Transports
Issued date:
Embargo end date: 9999-04-01
[EN] Traffic accidents data sets are usually imbalanced, where the number of instances classified under the killed or severe injuries class (minority) is much lower than those classified under the slight injuries class ...[+]
Subjects: Bayesian networks , Traffic accidents , Urban area , Imbalanced data set , SMOTE
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Accident Analysis & Prevention. (issn: 0001-4575 )
DOI: 10.1016/j.aap.2015.12.003
Publisher version: http://doi.org/10.1016/j.aap.2015.12.003
The authors are grateful to the Police Traffic Department in Jordan for providing the data necessary for this research. Griselda Lopez wishes to express her acknowledgement to the regional ministry of Economy, Innovation ...[+]
Type: Artículo



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