Duplicated Record Detection Based on Improved RBF Neural Network

摘要：

　　This paper presents a method based on modified Radial Basis Function(RBF)neural network to improve the accuracy and recall rate for detection of duplicated records.Firstly,key fields of records are clustered by Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and all records are classified to several classes which include duplicated records.Secondly,the similarity of corresponding fields of records in each class is computed using Jaro algorithm and duplicated records are labeled manually.Finally,Subtractive Clustering Method(SCM)and Particle Swarm Algorithm(PSO)are used to optimize the parameters of RBF neural network so that monitoring model of duplicated records is built.This method is tested with different datasets.The experimental results show that the accuracy and recall rate for the detection of duplicated records are improved significantly.

关键词： complex system Duplicated records RBF neural network PSO SCM parameter optimizing

作者: Xinting Liu Xiaodong Cai Bo Li Mingyao Chen

作者单位: School of Computer and Information Security,Guilin University of Electronic Technology,China Guilin Topintelligent Communication Technology Co.,Ltd,China

会议类型: 国际会议

会议名称: 2017 IEEE 2nd Advanced Information Technology,Electronic and Automation Control Conference(IAEAC 2017)(2017 IEEE 第2届先进信息技术、电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 2034-2037

在线出版日期: 2017-03-25（万方平台首次上网日期，不代表论文的发表时间）