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Using Principal Component Analysis to Solve a Class Imbalance Problem in Traffic Incident Detection
2013
Mathematical Problems in Engineering
High imbalances occur in real-world situations when a detection system needs to identify the rare but important event of a traffic incident. Traffic incident detection can be treated as a task of learning classifiers from imbalanced or skewed datasets. Using principal component analysis (PCA), a one-class classifier for incident detection is constructed from the major and minor principal components of normal instances. Experiments are conducted with a real traffic dataset collected from the A12
doi:10.1155/2013/524861
fatcat:edzvhcqg3jaddk4dxgwjocqhei