Abstract
As a derivative of DS (Dempster-Shafer) theory, ER (Evidential Reasoning) rule can be used as a combination strategy in ensemble learning to dig the classifier information. However, when ER rule are used to integrate classifiers, it is sometimes difficult to assign weight to classifier by traditional ER rule. In view of the above problems, ER rule are improved by using combination weighting instead of expert knowledge weighting in traditional ER rule in this paper, so as to reduce the loss of information and set a more reasonable weight. Firstly, the subjective weight and objective weight are combined to get the combination weight. Then the value range of weight in ER rule is studied, and the regularization of weight is discussed. Finally, the validity of the proposed weight setting method is verified through the classification of the English Bay weather image data set.
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Acknowledgments
In this paper, Yan-Zi Gao and Xu Cong have the same contribution. This work was supported in part by the Postdoctoral Science Foundation of China under Safety status assessment of large liquid launch vehicle based on deep belief rule base, in part by the Ph.D. research start-up Foundation of Harbin Normal University under Grant No. XKB201905, in part by the Natural Science Foundation of School of Computer Science and Information Engineering, Harbin Normal University, under Grant no. JKYKYZ202102.
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Xu, C., Zhou, ZJ., He, W., Zhu, H., Gao, YZ. (2021). Ensemble Learning Based on Evidential Reasoning Rule with a New Weight Calculation Method. In: Denœux, T., Lefèvre, E., Liu, Z., Pichon, F. (eds) Belief Functions: Theory and Applications. BELIEF 2021. Lecture Notes in Computer Science(), vol 12915. Springer, Cham. https://doi.org/10.1007/978-3-030-88601-1_15
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