Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Aggregation Algorithms for Neural Network Ensemble Construction
SBRN '02 Proceedings of the VII Brazilian Symposium on Neural Networks (SBRN'02)
An ensemble of neural networks for weather forecasting
Neural Computing and Applications
A Stochastic Optimization Approach for Parameter Tuning of Support Vector Machines
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Combination methods for ensembles of RBF networks
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Traffic-incident detection-algorithm based on nonparametric regression
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Neural Networks
Automatic traffic incident detection based on nFOIL
Expert Systems with Applications: An International Journal
Multiple extreme learning machines for a two-class imbalance corporate life cycle prediction
Knowledge-Based Systems
Ensemble learning for generalised eigenvalues proximal support vector machines
International Journal of Computer Applications in Technology
Hi-index | 12.05 |
This study presents the applicability of support vector machine (SVM) ensemble for traffic incident detection. The SVM has been proposed to solve the problem of traffic incident detection, because it is adapted to produce a nonlinear classifier with maximum generality, and it has exhibited good performance as neural networks. However, the classification result of the practically implemented SVM depends on the choosing of kernel function and parameters. To avoid the burden of choosing kernel functions and tuning the parameters, furthermore, to improve the limited classification performance of the real SVM, and enhance the detection performance, we propose to use the SVM ensembles to detect incident. In addition, we also propose a new aggregation method to combine SVM classifiers based on certainty. Moreover, we proposed a reasonable hybrid performance index (PI) to evaluate the performance of SVM ensemble for detecting incident by combining the common criteria, detection rate (DR), false alarm rate (FAR), mean time to detection (MTTD), and classification rate (CR). Several SVM ensembles have been developed based on bagging, boosting and cross-validation committees with different combining approaches, and the SVM ensemble has been tested on one real data collected at the I-880 Freeway in California. The experimental results show that the SVM ensembles outperform a single SVM based AID in terms of DR, FAR, MTTD, CR and PI. We used one non-parametric test, the Wilcoxon signed ranks test, to make a comparison among six combining schemes. Our proposed combining method performs as well as majority vote and weighted vote. Finally, we also investigated the influence of the size of ensemble on detection performance.