About: SICE: an improved missing data imputation technique

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	SICE: an improved missing data imputation technique
Creator	Sayed, Abu Hoque, Latiful Khan, Shahidul
source	Medline; PMC
abstract	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.
has issue date	2020-06-12(xsd:dateTime)
bibo:doi	10.1186/s40537-020-00313-w
bibo:pmid	32547903
has license	cc-by
sha1sum (hex)	c2cac7f5e4259806133546352c8e2f6e4953aa26
schema:url	https://doi.org/10.1186/s40537-020-00313-w
resource representing a document's title	SICE: an improved missing data imputation technique
has PubMed Central identifier	PMC7291187
has PubMed identifier	32547903
schema:publication	J Big Data
resource representing a document's body	covid:c2cac7f5e4259806133546352c8e2f6e4953aa26#body_text
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