.png){kind=icon}
返回
Missing data imputation in multivariate data by evolutionary algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chb.2010.06.026.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a proposal based on an evolutionary algorithm to impute missing observations in multivariate data. A genetic algorithm based on the minimization of an error function derived from their covariance matrix and vector of means is presented. All methodological aspects of the genetic structure are presented. An extended explanation of the design of the fitness function is provided. An application example is solved by the proposed method. (C) 2010 Elsevier Ltd. All rights reserved.
Keyword:
Missing data
Evolutionary optimization
Multivariate analysis
Multiple data imputation
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
9.0K
被引数:
5.8W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏A clinicopathological study of granulosa cell tumors of the ovary: Can morphology predict prognosis?
OAAI
OAAI分享收藏Data age based retransmission scheme for reliable control data exchange in platooning applications
PREAI
分享收藏分享收藏
查看更多内容