Belief rule based inference methodology for classification based on differential evolution algorithm

A new method, based on belief rule base(BRB) was proposed for constructing a classification system with high performance for classification problems. The existing belief rule base classification system (BRBCS) is flawed because its classification accuracy is limited to the partition number, the parameter training method needs the number of rules given in advance, and the reasoning process does not reflect the correlation between characteristics and results. The belief rule base inference method was thus proposed for classification based on the differential evolutionary algorithm (DEBRM) to solve the classification problems. The proposed method consists of two procedures: belief rule base classification system (BRBCS) and parameter training method. The new method first introduced the construction strategy of BRBCS to determine the number of rules. Then, belief reasoning method was adopted as the inference engine. Finally, the training model for classification which is combined with differential evolutionary algorithm was built. In the experiment analysis, the effectiveness of the method was validated by comparing it with the existing parameter training method, and the rationality of parameters training in comparison of other belief rule base methods for different number of partitions. The classification results show that the proposed method is effective and reasonable.