Abstract
As an extension of Dempster–Shafer (D-S) theory, the evidential reasoning (ER) rule can be used as a combination strategy in ensemble learning to deeply mine classifier information through decision-making reasoning. The weight of evidence is an important parameter in the ER rule, which has a significant effect on the result of ensemble learning. However, current research results on the weight of evidence are not ideal, leveraging expert knowledge to assign weights leads to the excessive subjectivity, and using sample statistical methods to assign weights relies too heavily on the samples, so the determined weights sometimes differ greatly from the actual importance of the attributes. Therefore, to solve the problem of excessive subjectivity and objectivity of the weights of evidence, and further improve the accuracy of ensemble learning based on the ER rule, we propose a novel combination weighting method to determine the weight of evidence. The combined weights are calculated by leveraging our proposed method to combine subjective and objective weights of evidence. The regularization of these weights is studied. Then, the evidential reasoning rule is used to integrate different classifiers. Five case studies of image classification datasets have been conducted to demonstrate the effectiveness of the combination weighting method.
Highlights
Most of the proposed weighting methods are only applicable to a specific engineering background and are not universal. ere is still a lack of a general method to determine the weight of evidence. erefore, to set a more general and reasonable evidence weight, this paper proposes to use the combination weighting method to set the weight of evidence in the evidential reasoning (ER) rule
In the field of ensemble learning based on the ER rule, problems include, a small number of classifiers in ensemble learning, difficulty in the quality judgement classifiers based on expert experience, objective weighting method that is too dependent on samples, and difficulty in setting a reasonable weight of evidence to improve the accuracy of ensemble learning. e combination weighting method is proposed to solve these problems. is method considers the subjective judgement of expert experience and the objective information of sample data, which overcomes the subjectivity or objectivity of the weight of evidence to a certain extent, making the weight of evidence more reasonable and significantly improve the recognition accuracy of the ensemble learning model
The weight of evidence is regularized, which further improves the effect of ensemble learning model based on the ER rule
Summary
Ensemble learning is a branch of machine learning, and using decision-making reasoning as a combination strategy helps ensemble learning to integrate better. e decisionmaking reasoning is the process of making a choice among many options and summarizing evidence to draw a conclusion, so it can help ensemble learning to integrate. e ER rule is a combination strategy [1], which can integrate classifiers through decision-making reasoning to obtain better results, so it is used as a decision-making strategy for ensemble learning in this paper. E ER rule is a combination strategy [1], which can integrate classifiers through decision-making reasoning to obtain better results, so it is used as a decision-making strategy for ensemble learning in this paper. Based on its advantages in improving uncertainty and reasoning, the ER rule can allow integrated models to obtain better results than strategies such as voting. (1) e combination weighting method is used to determine the weight of evidence in the ER rule It can further find the effective information in the process of combining evidence, take subjective factors and objective factors of evidence into consideration, and set more general and reasonable weight of evidence for the ER rule.
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