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Using Genetic Algorithm in Outlier Detection for Regression Model

Authors :
Zakariya Y. Algamal
Hamsa M.Thabet
Source :
مجلة التربية والعلم, Vol 27, Iss 3, Pp 136-142 (2018)
Publication Year :
2018
Publisher :
College of Education for Pure Sciences, 2018.

Abstract

Linear regression model is commonly used to analyze data from many fields. Sometimes the data under research contains outliers, and it is important that these outliers be identified in the course of the correct statistical analysis. In this article we used genetic algorithm (GA) with three type of objective functions,Akaike information criterion (AIC), Bayesian information criterion (BIC), and Hannan–Quinn information criterion (HQIC) to detect the problem of masking and swamping outliers in linear regression model . Two well – known data sets have been studied and we conclude that GA doing-well in detection these type of outliers when using AIC and HQIC comparingwithBIC.

Details

Language :
Arabic, English
ISSN :
1812125X and 26642530
Volume :
27
Issue :
3
Database :
Directory of Open Access Journals
Journal :
مجلة التربية والعلم
Publication Type :
Academic Journal
Accession number :
edsdoj.6e7713bc8ed145b8a8b12091c1ad003f
Document Type :
article
Full Text :
https://doi.org/10.33899/edusj.2018.159314