Performance analysis of machine learning algorithms for missing value imputation

Data mining requires a pre-processing task in which the data are prepared, cleaned, integrated, transformed, reduced and discretized for ensuring the quality. Missing values is a universal problem in many research domains that is commonly encountered in the data cleaning process. Missing values usua...

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Main Authors:	Zainal Abidin, Nadzurah, Ismail, Amelia Ritahani, Emran, Nurul Akmar
Format:	Article
Language:	English English English
Published:	Science and Information Organization 2018
Subjects:	T10.5 Communication of technical information
Online Access:	http://irep.iium.edu.my/65381/ http://irep.iium.edu.my/65381/ http://irep.iium.edu.my/65381/ http://irep.iium.edu.my/65381/1/65381_Performance%20analysis%20of%20machine%20learning.pdf http://irep.iium.edu.my/65381/2/65381_Performance%20analysis%20of%20machine%20learning_SCOPUS.pdf http://irep.iium.edu.my/65381/3/65381_Performance%20analysis%20of%20machine%20learning_WOS.pdf

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recordtype	eprints
spelling	iium-653812018-08-03T00:53:46Z http://irep.iium.edu.my/65381/ Performance analysis of machine learning algorithms for missing value imputation Zainal Abidin, Nadzurah Ismail, Amelia Ritahani Emran, Nurul Akmar T10.5 Communication of technical information Data mining requires a pre-processing task in which the data are prepared, cleaned, integrated, transformed, reduced and discretized for ensuring the quality. Missing values is a universal problem in many research domains that is commonly encountered in the data cleaning process. Missing values usually occur when a value of stored data absent for a variable of an observation. Missing values problem imposes undesirable effect on analysis results, especially when it leads to biased parameter estimates. Data imputation is a common way to deal with missing values where the missing value's substitutes are discovered through statistical or machine learning techniques. Nevertheless, examining the strengths (and limitations) of these techniques is important to aid understanding its characteristics. In this paper, the performance of three machine learning classifiers (K-Nearest Neighbors (KNN), Decision Tree, and Bayesian Networks) are compared in terms of data imputation accuracy. The results shows that among the three classifiers, Bayesian has the most promising performance. © 2015 The Science and Information (SAI) Organization Limited. Science and Information Organization 2018 Article PeerReviewed application/pdf en http://irep.iium.edu.my/65381/1/65381_Performance%20analysis%20of%20machine%20learning.pdf application/pdf en http://irep.iium.edu.my/65381/2/65381_Performance%20analysis%20of%20machine%20learning_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/65381/3/65381_Performance%20analysis%20of%20machine%20learning_WOS.pdf Zainal Abidin, Nadzurah and Ismail, Amelia Ritahani and Emran, Nurul Akmar (2018) Performance analysis of machine learning algorithms for missing value imputation. International Journal of Advanced Computer Science and Applications, 9 (6). pp. 442-447. ISSN 2158-107X E-ISSN 2156-5570 http://thesai.org/Downloads/Volume9No6/Paper_60-Performance_Analysis_of_Machine_Learning_Algorithms.pdf 10.14569/IJACSA.2018.090660
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	T10.5 Communication of technical information
spellingShingle	T10.5 Communication of technical information Zainal Abidin, Nadzurah Ismail, Amelia Ritahani Emran, Nurul Akmar Performance analysis of machine learning algorithms for missing value imputation
description	Data mining requires a pre-processing task in which the data are prepared, cleaned, integrated, transformed, reduced and discretized for ensuring the quality. Missing values is a universal problem in many research domains that is commonly encountered in the data cleaning process. Missing values usually occur when a value of stored data absent for a variable of an observation. Missing values problem imposes undesirable effect on analysis results, especially when it leads to biased parameter estimates. Data imputation is a common way to deal with missing values where the missing value's substitutes are discovered through statistical or machine learning techniques. Nevertheless, examining the strengths (and limitations) of these techniques is important to aid understanding its characteristics. In this paper, the performance of three machine learning classifiers (K-Nearest Neighbors (KNN), Decision Tree, and Bayesian Networks) are compared in terms of data imputation accuracy. The results shows that among the three classifiers, Bayesian has the most promising performance. © 2015 The Science and Information (SAI) Organization Limited.
format	Article
author	Zainal Abidin, Nadzurah Ismail, Amelia Ritahani Emran, Nurul Akmar
author_facet	Zainal Abidin, Nadzurah Ismail, Amelia Ritahani Emran, Nurul Akmar
author_sort	Zainal Abidin, Nadzurah
title	Performance analysis of machine learning algorithms for missing value imputation
title_short	Performance analysis of machine learning algorithms for missing value imputation
title_full	Performance analysis of machine learning algorithms for missing value imputation
title_fullStr	Performance analysis of machine learning algorithms for missing value imputation
title_full_unstemmed	Performance analysis of machine learning algorithms for missing value imputation
title_sort	performance analysis of machine learning algorithms for missing value imputation
publisher	Science and Information Organization
publishDate	2018
url	http://irep.iium.edu.my/65381/ http://irep.iium.edu.my/65381/ http://irep.iium.edu.my/65381/ http://irep.iium.edu.my/65381/1/65381_Performance%20analysis%20of%20machine%20learning.pdf http://irep.iium.edu.my/65381/2/65381_Performance%20analysis%20of%20machine%20learning_SCOPUS.pdf http://irep.iium.edu.my/65381/3/65381_Performance%20analysis%20of%20machine%20learning_WOS.pdf
first_indexed	2023-09-18T21:32:46Z
last_indexed	2023-09-18T21:32:46Z
_version_	1777412621278904320

Performance analysis of machine learning algorithms for missing value imputation

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