.png){kind=icon}
返回
SICE: an improved missing data imputation technique
.png){kind=icon}
OA
AI
DOI：10.1186/s40537-020-00313-w.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In data analytics, missing data is a factor that degrades performance. Incorrect imputation of missing values could lead to a wrong prediction. In this era of big data, when a massive volume of data is generated in every second, and utilization of these data is a major concern to the stakeholders, efficiently handling missing values becomes more important. In this paper, we have proposed a new technique for missing data imputation, which is a hybrid approach of single and multiple imputation techniques. We have proposed an extension of popular Multivariate Imputation by Chained Equation (MICE) algorithm in two variations to impute categorical and numeric data. We have also implemented twelve existing algorithms to impute binary, ordinal, and numeric missing values. We have collected sixty-five thousand real health records from different hospitals and diagnostic centers of Bangladesh, maintaining the privacy of data. We have also collected three public datasets from the UCI Machine Learning Repository, ETH Zurich, and Kaggle. We have compared the performance of our proposed algorithms with existing algorithms using these datasets. Experimental results show that our proposed algorithm achieves 20% higher F-measure for binary data imputation and 11% less error for numeric data imputations than its competitors with similar execution time.
Keyword:
Missing Data Imputation
Single Imputation
Multiple Imputation
MICE
Data Analytics
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:
6.4
论文数:
1.5K
被引数:
1.1W
机构
暂无机构信息
引用论文
Quantitative DNA variation between and within chromosome complements of Vigna species (Fabaceae).png)
Genetica
GeneticaIF0
PREAI
分享收藏Existence, uniqueness, and stability of stationary barotropic flow with forcing and dissipation
PREAI
分享收藏Motor conflict in Stroop tasks: Direct evidence from single-trial electro-myography and electro-encephalographyNeuroImageOAAI
NeuroImageIF0
OAAI分享收藏
查看更多内容