An enhanced noise resilient K-associated graph classifier
作者：
Highlights：
• We propose a non-parametric, noise resilient, graph-based classification algorithm.
• We employ relational data such as the degree of relevancy.
• We combine smaller components together to build larger ones.
• The algorithm is less noise sensitive than SVM and Decision Tree.
• The algorithm shows a superior performance in presence of different levels of noise.
摘要
•We propose a non-parametric, noise resilient, graph-based classification algorithm.•We employ relational data such as the degree of relevancy.•We combine smaller components together to build larger ones.•The algorithm is less noise sensitive than SVM and Decision Tree.•The algorithm shows a superior performance in presence of different levels of noise.
论文关键词：Graph-based classifier,Noisy samples,K-associated graph
论文评审过程：Available online 29 June 2015, Version of Record 28 July 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.06.044
