Belief Rule-Based (BRB) model has been widely used for complex system classification and prediction. However, excessive antecedent attributes will cause the combinatorial explosion problem, which restricts the applicability of the BRB model to high-dimensional problems. In this paper, we propose an Ensemble-BRB model with the use of the bagging framework to downsize the belief rule base and avoid the combinatorial explosion problem. The kernel of the Ensemble-BRB model is to generate several weak BRBs orderly, each of which only consists of a subset of antecedent attributes. Different combination methods can be used to integrate these weak BRBs coherently for classification and prediction respectively. Four benchmark problems are tested to validate the efficiency of the proposed Ensemble-BRB model in classification, and a real case on the health index prediction of engines proves the feasibility of the Ensemble-BRB model in prediction. The results on both classification and prediction show that the Ensemble-BRB model can effectively downsize the BRB as well as reach a high modeling accuracy.

基于置信规则的（BRB）模型已被广泛用于复杂的系统分类和预测。但是，过多的先决条件属性将导致组合爆炸问题，从而将BRB模型的适用性限制在高维问题上。在本文中，我们提出了一个使用袋装框架的Ensemble-BRB模型，以缩小信念规则库的规模并避免组合爆炸问题。Ensemble-BRB模型的核心是有序地生成多个弱BRB，每个弱BRB仅由先前属性的子集组成。可以使用不同的组合方法将这些弱BRB相干集成，分别用于分类和预测。测试了四个基准问题，以验证所提出的Ensemble-BRB模型在分类中的效率，发动机健康指标预测的实际案例证明了Ensemble-BRB模型在预测中的可行性。分类和预测结果均表明，Ensemble-BRB模型可以有效地缩小BRB的尺寸，并达到较高的建模精度。