基于三支决策的增量标签传播算法

Incremental Tag Propagation Algorithm Based on Three-way Decision

三支决策(Three-Way Decision,3WD)作为一种新的粒计算方法,在处理不确定和不精确问题上具有独特的优势。针对标签传播算法(Label Propagation Algorithm,LPA)在节点更新过程中存在的较高随机不确定性和冗余性问题,提出了基于三支决策的增量标签传播算法(3WD_ILPA)。首先,给出了邻接模糊信息测度的概念和计算方法,并用于生成任意两节点间的概率转移矩阵。然后,将三支决策融入节点的动态更新过程,并把迭代更新后准确率最高的节点逐步增量添加到下一循环过程,直至收敛。此外,给出了3WD_ILPA算法的详细流程。最后,在ABIDE数据集上进行孤独症(Autistic Spectrum Disorder,ASD)识别实验,与传统机器学习、深度学习和迁移学习等方法的对比结果表明,所提方法具有更高的准确率。

As a new method of granular computing,the three-way decision(3WD)has unique advantages in dealing with uncertain and imprecise problems.Aiming at the high random uncertainty and redundancy of the label propagation algorithm(LPA)in the node update process,an incremental label propagation algorithm based on the three-way decision(3WD_ILPA)is proposed.First,the concept and calculation method of adjacency fuzzy information measure are given and used to generate the probability transfer matrix between any two nodes.Then,the three-way decision is integrated into the dynamic update process,and the node with the highest precision is added to the next periodic iteration until convergence.Furthermore,the algorithm flow of 3WD_ILPA is given in detail.Finally,the autism(ASD)recognition experiment is carried out on the ABIDE data set.By comparing with traditional machine learning,deep learning and transfer learning methods,the results show that the proposed method has higher accuracy.