Using the average method and the voting method as a combination strategy can't make full use of the effective information of the base classifiers in ensemble learning, and the weights of base classifiers set with the volatility are imprecise and inappropriate. The above problems will reduce the effect of ensemble learning. In order to further improve the performance of ensemble learning, this paper proposed an ensemble learning method, which used evidence reasoning(ER) rules as a combination strategy and used diversity empowerment method set up the weights of the base classifiers. Firstly, the model used multiple deep learning models as the base classifiers and the ER rules as the combination strategy to construct the basic structure of ensemble learning. Then, it calculated the differences of each base classifier with respect to other base classifiers by the diversity measure method. Finally, it used the results of the differences normalization of the base classifiers as the weights of the base classifiers. Through the classification experiments of multiple image datasets, the experimental results show that the proposed method is more accurate and stable than other methods, which proves that this method can make full use of the effective information of the base classifiers, and the diversity weighting method is more accurate.

[2022-11-08] Accepted Paper /doc/2022.08.0429/v1

[2023-04-05] Printed Article /doc/2022.08.0429/v2