吉林大学学报（理学版）2024，Vol.62Issue(4)：915-922,8.DOI:10.13413/j.cnki.jdxblxb.2023389
引入激活扩散的类分布关系近邻分类器
Introducing Class-Distribution Relational Neighbor Classifier with Activation Spreading
摘要
Abstract
Aiming at the limitation of the simplifying the processing of homophily relational classifiers based on first-order Markov assumption,when constructing the class vector and reference vector in the class-distribution relational neighbor classifier,we introduced the activation spreading algorithm of local graph ranking,combined with the relaxation labeling collective inference method.By appropriately expanding the range of neighboring nodes during classification,we increased the homophily of nodes to be classified in network data,thereby reducing the error rate of classification.The comparative experimental results show that this method expands the neighborhood of nodes to be classified,and has good classification accuracy on network data.关键词
人工智能/网络数据分类/激活扩散/类分布关系近邻分类器/协作推理Key words
artificial intelligence/network data classification/activation spreading/class-distribution relational neighbor classifier/collective inference分类
信息技术与安全科学引用本文复制引用
董飒,欧阳若川,徐海啸,刘杰,刘大有,李婷婷,王鑫禄..引入激活扩散的类分布关系近邻分类器[J].吉林大学学报（理学版）,2024,62(4):915-922,8.基金项目
国家自然科学基金(批准号:61502198). （批准号:61502198）
