.png){kind=icon}
返回
Constructing Interpretable Belief Rule Bases Using a Model-Agnostic Statistical Approach
.png){kind=icon}
PRE
AI
DOI：10.1109/TFUZZ.2024.3416448.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Belief rule base (BRB) has attracted considerable interest due to its interpretability and exceptional modeling accuracy. Generally, BRB construction relies on prior knowledge or historical data. The limitations of knowledge constrain the knowledge-based BRB and are unsuitable for use in large-scale rule bases. Data-driven techniques excel at extracting model parameters from data, thus significantly improving the accuracy of BRB. However, the previous data-based BRBs neglected the study of interpretability, and some still depend on prior knowledge or introduce additional parameters. All these factors make the BRB highly problem-specific and limit its broad applicability. To address these problems, a model-agnostic statistical BRB (MAS-BRB) modeling approach is proposed in this article. It adopts an MAS methodology for parameter extraction, ensuring that the parameters both fulfill their intended roles within the BRB framework and accurately represent complex, nonlinear data relationships. A comprehensive interpretability analysis of MAS-BRB components further confirms their compliance with established BRB interpretability standards. Experiments conducted on multiple public datasets demonstrate that MAS-BRB not only achieves improved modeling performance but also shows greater effectiveness compared to existing rule-based and traditional machine learning models.
Keyword:
Data models
Knowledge based systems
Parameter extraction
Fuzzy systems
Feature extraction
Explosions
Cognition
Belief rule base (BRB)
data-driven
explainable artificial intelligence (XAI)
interpretability
model-agnostic
期刊
IF:
11.9
论文数:
5.0K
被引数:
2.9W
机构
引用论文
.png){kind=icon}
A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge基于不确定训练数据和专家知识的混合置信规则分类系统
OAAI
OAAI分享收藏A New Prediction Model Based on Belief Rule Base for System's Behavior Prediction基于信念规则库的系统行为预测新模型
PREAI
分享收藏
A fast belief rule base generation and reduction method for classification problems分类问题的一种快速置信规则库生成与约简方法
PREAI
分享收藏
查看更多内容