# A Classification Model Based on Interval Rule Inference Network with Interpretability
## 2 Related Work  
A rule-based system is an interpretability artificial intelligence technology. Rules can manipulate and incorporate new and existing knowledge, and adjust the behavior of the system through rules. At present, rule-based systems are mainly divided into three types: belief rule base (BRB) [9], extended belief rule base (EBRB) [10], and interval belief rule base (IBRB) [13].  
The BRB, as an interpretable language model, has the ability to process qualitative and quantitative information under uncertainty and has developed rapidly in recent years. [PERSON] et al. [18] proposed a fast and accurate BRB generation and reduction method, which grouped and combined similar rules in BRBs and removed the redundancy-based belief rule to alleviate the combinatorial explosion problem of BRBs. [PERSON] et al. [19] proposed hierarchical BRB using a multi-layer tree structure (MTS-BRB), which effectively overcomes the combinatorial explosion problem of BRBs by introducing a multi-layer tree structure. [PERSON] et al. [20] proposed to learn a compact and accurate belief rule base with the help of the decision tree building technique. By introducing a decision tree build technology that jointly implements feature selection and model construction, a compact and accurate BRB was obtained.  
On the basis of the BRB, [PERSON] et al. [10] further proposed an extended BRB, namely, EBRB. EBRB not only inherits the advantages of the BRB inference model, but also has an effective and concise modeling method, so it has attracted the attention of many researchers. [PERSON] et al. [5] introduced data envelopment analysis to evaluate the efficiency of each rule in EBRB, effectively reducing the number of EBRB rules. [PERSON] et al. [21] proposed a rule activation method based on nearest neighbor propagation, which significantly improved the accuracy of EBRB inference and efficiency. [PERSON] et al. [22] proposed a rule generation and activation method for the extended belief rule system based on an improved decision tree, and constructed an effective classification model. This model effectively solved the problems of low EBRB inference efficiency and inconsistent rule activation.  
However, since neither EBRB nor BRB considers interval uncertainty, they are limited in handling this type of uncertainty. To solve this problem, some researchers proposed a new rule-based system called IBRB. IBRB effectively handles various types of interval uncertainty by embedding interval-valued belief degrees in both the antecedents and consequents of each rule. Currently, IBRB is still in the improvement and development stage, and most of the research focuses on how to deal with the interval uncertainty caused by interval data, interval reference values, and interval belief degree [7; 11; 12; 13].  
It can be found from the above-mentioned related works on rule-based systems that the IBRB is a more flexible and rational rule-based system for expressing uncertain information and knowledge. However, it is a challenge for experts to set multiple parameters manually in IBRB, especially when the number of rules is large. Hence, the present study focuses on using a method that can automatically generate IBRBs to solve the highly complex problem of manual parameter setting by IBRB experts.