QIN Hui-chao (秦慧超), HU Hong-ping (胡红萍), BAI Yan-ping (白艳萍)
(School of Science, North University of China, Taiyuan 030051, China)
Abstract：This paper has concluded six features that belong to passenger vehicle types based on genetic algorithm(GA) of feature selection. We have obtained an optimal feature subset, including length, ratio of width and length, and ratio of height and length. And then we apply this optimal feature subset as well as another feature set, containing length, width and height, to the network input. Back-propagation(BP) neural network and support vector machine(SVM) are applied to classify the passenger vehicle type. There are four passenger vehicle types. This paper selects 400 samples of passenger vehicles, among which 320 samples are used as training set (each class has 80 samples) and the other 80 samples as testing set, taking the feature of the samples as network input and taking four passenger vehicle types as output. For the test, we have applied BP neural network to choose the optimal feature subset as network input, and the results show that the total classification accuracy rate can reach 96%, and the classification accuracy rate of first type can reach 100%. In this condition, we obtain a conclusion that this algorithm is better than the traditional ones[9].
Key words：genetic algorithm(GA); feature selection; back-propagation(BP) network; passenger vehicles type
CLD number： TP183 Document code： A
Article ID： 1674-8042(2012)03-0251-04 doi： 10.3969/j.issn.1674-8042.2012.03.011
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