Granular computing (GrC) is an essential tool to solve human real problem since the information granules is close to human perception schemes. In GrC, both classification accuracy and interpretability play significant roles. Fuzzy rule (FR) based classification systems are effective methods solving this problem. However, the accuracy of FR may be decreased when solving some complex application. In this paper, a novel model called FR–KDE integrating the FR and kernel density estimation (KDE) in the framework of Dempster–Shafer evidence theory is proposed to deal with the classification problem. By fusing the result of FR and KDE via the Dempster’s combination rule, it can reduce the uncertainty of FR and obtain better accuracy. To illustrate the effect of the FR–KDE approach, it is applied to the medical data classification problem. Experimentally, the results demonstrate that the FR–KDE method is effective in handling biomedical data classification problems.

粒度计算（GrC）是解决人类实际问题的必不可少的工具，因为信息粒度接近于人类的感知方案。在GrC中，分类准确性和可解释性都起着重要作用。基于模糊规则（FR）的分类系统是解决此问题的有效方法。但是，在解决某些复杂应用时，FR的精度可能会降低。本文提出了一种在Dempster-Shafer证据理论框架下将FR和核密度估计（KDE）相结合的新颖模型FR-KDE，以解决分类问题。通过使用Dempster的组合规则融合FR和KDE的结果，可以减少FR的不确定性并获得更好的精度。为了说明FR-KDE方法的效果，将其应用于医学数据分类问题。实验上，