A rule-based system is a widely used artificial intelligence system that employs a set of rules to make decisions. The belief rule-based (BRB) classification system is an extension of fuzzy rule-based (FRB) system that handles uncertainty and imprecision in classification tasks by incorporating Dempster-Shafer evidence theory and fuzzy set theory. However, the BRB classification system suffers from the combinatorial explosion and high time complexity problems. To solve these issues, the fuzzy unordered rule induction algorithm (FURIA) is introduced into the BRB classification systems to design a novel BRB classification system in this paper. FURIA is computationally less complex and has superior classification performance. The proposed system outperforms BRB classification systems in terms of classification performance and significantly reduces the number of rules and conditions. Furthermore, the proposed system offers a more effective solution than FURIA. To evaluate the validity and superiority of the proposed system, we conduct two classification experiments, comparing it with five traditional classifiers and seven rule-based systems, respectively, in terms of classification performance and interpretability.

中文翻译：

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使用模糊无序规则归纳算法的基于置信规则的分类系统

基于规则的系统是一种广泛使用的人工智能系统，它采用一组规则来做出决策。基于置信规则（BRB）的分类系统是基于模糊规则（FRB）系统的扩展，它通过结合 Dempster-Shafer 证据理论和模糊集理论来处理分类任务中的不确定性和不精确性。然而，BRB分类系统存在组合爆炸和时间复杂度高的问题。为了解决这些问题，本文将模糊无序规则归纳算法（FURIA）引入到BRB分类系统中，设计了一种新颖的BRB分类系统。 FURIA 的计算复杂度较低，并且具有卓越的分类性能。该系统在分类​​性能方面优于 BRB 分类系统，并显着减少了规则和条件的数量。此外，所提出的系统提供了比 FURIA 更有效的解决方案。为了评估所提出系统的有效性和优越性，我们进行了两次分类实验，分别与五个传统分类器和七个基于规则的系统在分类​​性能和可解释性方面进行比较。