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大数据网络信息系统缺失优化检测仿真研究 被引量:2

Simulation Research on Optimization of Large Data Network Information System
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摘要 对大数据网络信息系统的数据缺失的有效检测,能够提升对大数据分析的完整性和全面性。对数据缺失值的检测,需要提取每个数据属性的深度特征,进行缺失值的预测,完成对数据缺失值进行检测。传统方法度量不同数据缺失样本的相关性,构造数据缺失样本的相似度矩阵,但忽略了对缺失值进行预测,导致检测精度低。提出基于支持向量机的大数据网络信息系统数据缺失值检测方法。融合于深度学习理论将检测自动编码机定义为基础模块,从完整数据子集中提取不完全数据集中每个数据属性的深度特征,利用支持向量机方法,将缺失值检测划分为连续、类别属性缺失值检测两种情况,进行缺失值的预测,并完成了对大数据网络信息系统数据缺失值的检测。仿真证明,所提方法检测精度高,能够有效检测缺失数据。 Based on support vector machine ( SVM), this article proposes a detection method for value of data missing in information system of large data network. Our research defined automatic coding machine of detection as basic module integrated with deep learning theory and extracted depth feature of each data attribute in incomplete data set from data subset. The research used SVM method to divide detection of missing value into two cases of continuous and categorical attribute and predicted the missing value. Simulation proves that the method has higher detection pre- cision and can detect the missing data effectively.
出处 《计算机仿真》 北大核心 2017年第9期428-431,共4页 Computer Simulation
基金 基金项目:OAIS的电子文件生命周期可信安全管理平台(15A520113)
关键词 网络信息系统 数据缺失 优化检测 Network information system Data missing Optimal detection
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