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Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection
Joint Authors
Liu, Qingchao
Chen, Shuyan
Zhao, Kangjia
Lu, Jian
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-04-28
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
This study presents the applicability of the Naïve Bayes classifier ensemble for traffic incident detection.
The standard Naive Bayes (NB) has been applied to traffic incident detection and has achieved good results.
However, the detection result of the practically implemented NB depends on the choice of the optimal threshold, which is determined mathematically by using Bayesian concepts in the incident-detection process.
To avoid the burden of choosing the optimal threshold and tuning the parameters and, furthermore, to improve the limited classification performance of the NB and to enhance the detection performance, we propose an NB classifier ensemble for incident detection.
In addition, we also propose to combine the Naïve Bayes and decision tree (NBTree) to detect incidents.
In this paper, we discuss extensive experiments that were performed to evaluate the performances of three algorithms: standard NB, NB ensemble, and NBTree.
The experimental results indicate that the performances of five rules of the NB classifier ensemble are significantly better than those of standard NB and slightly better than those of NBTree in terms of some indicators.
More importantly, the performances of the NB classifier ensemble are very stable.
American Psychological Association (APA)
Liu, Qingchao& Lu, Jian& Chen, Shuyan& Zhao, Kangjia. 2014. Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-467781
Modern Language Association (MLA)
Liu, Qingchao…[et al.]. Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection. Mathematical Problems in Engineering No. 2014 (2014), pp.1-16.
https://search.emarefa.net/detail/BIM-467781
American Medical Association (AMA)
Liu, Qingchao& Lu, Jian& Chen, Shuyan& Zhao, Kangjia. Multiple Naïve Bayes Classifiers Ensemble for Traffic Incident Detection. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-16.
https://search.emarefa.net/detail/BIM-467781
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-467781