SICE: an improved missing data imputation technique
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 stakeh...
Main Authors: | Shahidul Islam Khan, Abu Sayed Md Latiful Hoque |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2020-06-01
|
Series: | Journal of Big Data |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s40537-020-00313-w |
Similar Items
-
Comparison of Single and MICE Imputation Methods for Missing Values: A Simulation Study
by: Deni, SM, et al.
Published: (2021) -
Comparison of single and mice imputation methods for missing values: A simulation study
by: Deni, S.M, et al.
Published: (2021) -
Imputation techniques for non-ordered categorical missing data
by: Karangwa, Innocent
Published: (2016) -
The Effects of Missing Data Characteristics on the Choice of Imputation Techniques
by: Oyekale Abel Alade, et al.
Published: (2020-05-01) -
New Trends in Evidence-based Statistics: Data Imputation Problems
by: N. V. Kovtun, et al.
Published: (2019-12-01)