PSO-AdaBoost training algorithm based on EREF
基于EREF的PSO-AdaBoost训练算法*

Focusing on the disadvantage of the AdaBoost algorithm based on PSO, this paper mainly analyzed the issue that the traditional target function could not adapt to the problem of weak classifiers selection when they had the same minimum error rate and a new method was advanced to avoid the problem. The new method used the absolute difference between the threshold and feature to measure the extent of misclassification and combined with the relative entropy principle as the fitness function. In this way, the new fitness function could select the best weak classifiers more accuracy. Experimental results indicate that the method can achieve both better performance and less generalization error.