Abstract
When using evidence theory to identify targets, how to generate basic probability assignment (BPA) based on the collected data is still an open issue. Based on this problem, a novel method to generate BPA based on membership function and principal component analysis (PCA) is proposed. First, this paper proposed a novel membership function and divide the data set into the training set and testing set, the membership model of each attribute is constructed based on the training set data. Secondly, the testing set sample is input to obtain the initial BPA of each attribute. Thirdly, the contribution rate of each attribute is used by PCA. Finally, the final BPA is obtained by discounting the initial BPA according to the contribution rate. The results of the experiment have demonstrated the classification accuracy under the Iris dataset is higher than other methods, and the average recognition rate of the Iris dataset is 97.3%.
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