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- W1977151806 abstract "This paper presents the application of a grafted decision tree for traffic incident detection. The detection performance is evaluated by the criteria, including: (1) detection rate; (2) false alarm rate; (3) mean time to detection; (4) classification rate; and, (5) the area under the curve of the receiver operating characteristic. Different from the general decision tree, a grafted decision tree model is able to increase the probability of rightly classifying instances that fall outside the areas covered by the training data set. The detection performance of the grafted decision tree was compared to radical basis function neural networks (RBFNN) and multilayer feed forward neural networks (MLFNN) which yielded superior incident detection performance in the previous studies. The experimental results indicate that the grafted decision tree is competitive with RBFNN and MLFNN." @default.
- W1977151806 created "2016-06-24" @default.
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- W1977151806 date "2014-06-24" @default.
- W1977151806 modified "2023-10-16" @default.
- W1977151806 title "Grafted Decision Tree for Freeway Incident Detection" @default.
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- W1977151806 doi "https://doi.org/10.1061/9780784413623.046" @default.
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