Systematic Review of Using Machine Learning in Imputing Missing Values-Reference-Cited by-同舟云学术

Systematic Review of Using Machine Learning in Imputing Missing Values

Published:2022 Issue: Volume:10 Page:44483-44502
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alabadla Mustafa¹^ORCID,Sidi Fatimah¹^ORCID,Ishak Iskandar¹^ORCID,Ibrahim Hamidah¹^ORCID,Affendey Lilly Suriani¹^ORCID,Che Ani Zafienas¹^ORCID,Jabar Marzanah A.²^ORCID,Bukar Umar Ali²^ORCID,Devaraj Navin Kumar³^ORCID,Muda Ahmad Sobri⁴^ORCID,Tharek Anas⁴^ORCID,Omar Noritah⁵^ORCID,Jaya M. Izham Mohd⁶^ORCID

Affiliation:

1. Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia

2. Department of Software Engineering and Information System, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia

3. Department of Family Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia

4. Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia

5. Department of English, Faculty of Modern Languages and Communication, Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia

6. Department of Software Engineering, Faculty of Computing, Universiti Malaysia Pahang (UMP), Pekan, Pahang, Malaysia

Funder

Ministry of Higher Education through the Fundamental Research Grant Scheme

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09762231.pdf?arnumber=9762231

Reference113 articles.

1. A novel framework for imputing large gaps of missing values from time series sensor data of marine machinery systems

2. Information granule-based classifier: A development of granular imputation of missing data

3. An industrial missing values processing method based on generating model

4. Information-decomposition-model-based missing value estimation for not missing at random dataset

5. Missing data techniques in classification for cardiovascular dysautonomias diagnosis

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