降低相似度碰撞的证据融合方法

针对相似度碰撞引发证据融合结果错误的问题，提出一种新的证据融合方法。首先，提取证据的焦元序列特征并将其转化为排序矩阵以弥补证据相似度易碰撞的不足；其次，联合证据的排序矩阵和信息熵完成对证据权重的确定；最后，生成归一化证据（MAE）并使用Dempster融合公式将MAE融合n-1次获得最终的结果。基于在线的鸢尾花数据集对证据平均融合方法、余弦相似度证据融合方法、证据距离融合方法和证据信誉度融合方法进行了花类型识别准确性的F-Score对比，上述四种方法的F-Score分别为0.84、0.88、0.88和0.88，而所提方法的F-Score为0.91。实验结果表明，所提方法的决策准确率更高，融合结果更加可靠，能为证据决策提供了有效的解决方案。

Evidence combination rule with similarity collision reduced

Aiming at the problem of decision error caused by similarity collision in evidence theory, a new combination rule for evidence theory was proposed. Firstly, the features of focal-element sequence in evidence were extracted and converted into a sort matrix to reduce similarity collision. Secondly, the weight of each evidence was determined based on sort matrix and information entropy. Finally, the Modified Average Evidence (MAE) was generated based on the evidence set and evidence weight, and the combination result was obtained by combing MAE for n-1 times by using Dempster combination rule. The experimental results on the online dataset Iris show that the F-Score of average-based combination rule, similarity-based combination rule, evidence distance-based combination rule, evidence-credit based combination rule and the proposed method are 0.84, 0.88, 0.88, 0.88 and 0.91. Experimental results show that the proposed method has higher accuracy of decision making and more reliable combination results, which can provide an efficient solution for decision-making based on evidence theory.