TY  - JOUR
AB  - Purpose– Many applications in intelligent transportation demand accurate categorization of vehicles. The purpose of this paper is to propose a working image-based vehicle classification system. The first component vehicle detection is implemented by applying Dalal and Triggs's histograms of oriented gradients features and linear support vector machine (SVM) classifier. The second component vehicle classification, which is the emphasis of this paper, is accomplished by an improved stacked generalization. As an effective ensemble learning strategy, stacked generalization has been proposed to combine multiple models using the concept of a meta-learner. However, it was found that the well-known meta-learning scheme multi-response linear regression (MLR) for stacked generalization performs poorly on the vehicle classification. Design/methodology/approach– A new meta-learner is then proposed based on kernel principal component regression (KPCR). The stacked generalization scheme consists of a heterogeneous classifier ensemble with seven base classifiers, i.e. linear discriminant classifier, fuzzy k-nearest neighbor, logistic regression, Parzen classifier, Gaussian mixture model, multiple layer perceptron and SVM. Findings– Experimental results using more than 2,500 images from four types of vehicles (bus, light truck, car and van) demonstrated the effectiveness of the proposed approach. The improved stacked generalization produced consistently better results when compared to any of the single base classifier used and four other beta learning algorithms, including MLR, majority voting, logistic regression and decision template. Originality/value– With the seven base classifiers, the KPCR-based stacking offers a performance of 96 percent accuracy and 95 percent κ coefficient, thus exhibiting promising potentials for real-world applications.
VL  - 7
IS  - 4
SN  - 1756-378X
DO  - 10.1108/IJICC-06-2013-0030
UR  - https://doi.org/10.1108/IJICC-06-2013-0030
AU  - Zhang Bailing
AU  - Pan Hao
PY  - 2014
Y1  - 2014/01/01
TI  - Vehicle identification by improved stacking via kernel principal component regression
T2  - International Journal of Intelligent Computing and Cybernetics
PB  - Emerald Group Publishing Limited
SP  - 415
EP  - 435
Y2  - 2024/04/25
ER  -