An Efficient Traffic Incident Detection and Classification Framework by Leveraging the Efficacy of Model Stacking

Algorithm 1

Model stacking [E-IDC (X, C)].

	Input:
	1. “X” = S[i][j] is a matrix of training dataset with “i” instances and “j” independent variables
	2. “C” = {Set of individual classifiers selected under study i.e., Naïve Bayes, Decision Table…. J48}
	Procedure Begin
	1. Split “X” to “m” datasets
	1.1. Assign binary labels for each severity class in “Xm”
	2. FOR “m” = 1 to 9
	2.1. Apply classification algorithm from “C” on “Xm”
	2.1.1. Predict severity labels for “i” rows of “X”
	2.1.2. Write predicted severity labels in “X′”
	3. Split “X′” to “l” datasets
	3.1. Assign binary labels for each incident type class in “Xl”
	4. FOR l = 1 to 3
	4.1. Apply classification algorithm from “C” on “Xl ”
	4.1.1. Predict type labels for “i” rows of “X′”
	4.1.2. Apply evaluation criteria to assess the F-measure of the classifier
	Procedure End
	Output: The outperform incident predictor with best F-measure along with severity and type labels