Performance analysis of machine learning algorithms for missing value imputation

Ismail, Amelia Ritahani and Zainal Abidin, Nadzurah and Emran, Nurul Akmar (2018) Performance analysis of machine learning algorithms for missing value imputation. International Journal Of Advanced Computer Science And Applications(Ijacsa),Volume 9 Issue 6,2018., 9. pp. 442-447. ISSN 2158-107X

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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.

Item Type:	Article
Uncontrolled Keywords:	Data mining, Imputation, Machine learning, K-nearest neighbors, Decision tree, Bayesian networks
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics
Divisions:	Faculty of Information and Communication Technology
Depositing User:	Mohd. Nazir Taib
Date Deposited:	12 Jun 2019 07:14
Last Modified:	17 Jul 2023 16:06
URI:	http://eprints.utem.edu.my/id/eprint/21967
Statistic Details:	View Download Statistic

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