Performance analysis of machine learning algorithms for missing value imputation

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

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Abstract

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.

Item Type:	Article (Journal)
Additional Information:	4296/65381
Uncontrolled Keywords:	Data Mining; Imputation; Machine Learning; KNearest Neighbors; Decision Tree; Bayesian Networks
Subjects:	T Technology > T Technology (General) > T10.5 Communication of technical information
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	03 Aug 2018 08:51
Last Modified:	03 Aug 2018 08:53
URI:	http://irep.iium.edu.my/id/eprint/65381

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