摘要
为了提高数据库重复记录检测效果,提出了基于深度学习的数据库重复记录检测算法。首先分析当前数据库重复记录检测的进展,找到引起数据库重复记录检测效果差的原因,然后利用深度学习算法中的支持向量机对数据库重复记录检测进行建模,并引入量子粒子群算法优化支持向量机参数,最后进行了数据库重复记录检测仿真实验,结果表明,文中算法的数据库重复记录检测正确率和效率均很高,数据库重复记录检测结果明显优于当前其它算法。
In order to improve the effect of database duplicate record detection and optimization,a database duplicate record detection algorithm based on deep learning is proposed.Firstly,the paper analyzes the development of duplicate record detection and optimization in database,finds out the reasons for the poor effect of duplicate record detection and optimization in database,then the SVM in deep learning algorithm models the duplicate record detection and optimization in database,and introduces the quantum particle swarm optimization algorithm to optimize the parameters of SVM,and finally carries out the simulation experiment of duplicate record detection in database.The results of testing show that the recall rate and accuracy rate of the algorithm are very high,and the database duplicate record detection results are obviously better than other current algorithms.
作者
陶姿邑
TAO Ziyi(Information Construction Management Office,Shanxi University of Chinese Medicine,Xianyang 712046,China)
出处
《微型电脑应用》
2020年第12期174-176,共3页
Microcomputer Applications
关键词
数据库
重复记录检测
深度学习
量子粒子群算法
databases
duplicate record detection
deep learning
quantum particle swarm algorithm
