基于RReliefF特征选择算法的复杂网络链接分类

摘要

This paper researches the problem of link classification based on complex network features.Aiming at the situation that the original featurehas large noise and redundancy,this paper proposes an improved link classification model based on RReliefF feature selection algorithm.The feature associated with the link information is constructed from the local and global dimensions.RReliefF algorithm is introduced to select features,and the regression classification is carried out by the Partial Least Squares(PLS)method.The result of experiments on artificial datasets and real datasets show that,the model can screendiscriminativecharacteristicto improve the quality of link classification.They alsoprovide a new idea for complex network link classification of supervised learning.%研究基于复杂网络特征的链接分类问题,针对原始特征噪声信息多、冗余度大的现象,在RReliefF特征选择算法的基础上,提出一种改进的链接分类模型.从局部和全局2个维度构建与链接相关联的特征信息,引入RReliefF算法对特征进行选择并采用偏最小二乘法实现回归分类.在人工数据集和真实数据集上的实验结果表明,该模型能筛选出具有判别性的特征,提高链接分类质量,为监督学习的复杂网络链接分类提供一种新思路.