Using Genetic Algorithm in Outlier Detection for Regression Model

Using Genetic Algorithm in Outlier Detection for Regression Model

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 o...

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Bibliographic Details
Main Authors: Zakariya Y. Algamal, Hamsa M.Thabet
Format: Article
Language:Arabic
Published: College of Education for Pure Sciences 2018-06-01
Series:مجلة التربية والعلم
Subjects:
outliers king
swamping
genetic algorithm
information criteria
Online Access:https://edusj.mosuljournals.com/article_159314_39b5268029f82b3c47f9622965a2dfab.pdf
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spelling doaj-6e7713bc8ed145b8a8b12091c1ad003f2020-11-25T01:10:19ZaraCollege of Education for Pure Sciencesمجلة التربية والعلم1812-125X2664-25302018-06-0127313614210.33899/edusj.2018.159314159314Using Genetic Algorithm in Outlier Detection for Regression ModelZakariya Y. AlgamalHamsa M.ThabetLinear 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.https://edusj.mosuljournals.com/article_159314_39b5268029f82b3c47f9622965a2dfab.pdfoutliers kingswampinggenetic algorithminformation criteria
collection DOAJ
language Arabic
format Article
sources DOAJ
author Zakariya Y. Algamal
Hamsa M.Thabet
spellingShingle Zakariya Y. Algamal
Hamsa M.Thabet
Using Genetic Algorithm in Outlier Detection for Regression Model
مجلة التربية والعلم
outliers king
swamping
genetic algorithm
information criteria
author_facet Zakariya Y. Algamal
Hamsa M.Thabet
author_sort Zakariya Y. Algamal
title Using Genetic Algorithm in Outlier Detection for Regression Model
title_short Using Genetic Algorithm in Outlier Detection for Regression Model
title_full Using Genetic Algorithm in Outlier Detection for Regression Model
title_fullStr Using Genetic Algorithm in Outlier Detection for Regression Model
title_full_unstemmed Using Genetic Algorithm in Outlier Detection for Regression Model
title_sort using genetic algorithm in outlier detection for regression model
publisher College of Education for Pure Sciences
series مجلة التربية والعلم
issn 1812-125X
2664-2530
publishDate 2018-06-01
description 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.
topic outliers king
swamping
genetic algorithm
information criteria
url https://edusj.mosuljournals.com/article_159314_39b5268029f82b3c47f9622965a2dfab.pdf
work_keys_str_mv AT zakariyayalgamal usinggeneticalgorithminoutlierdetectionforregressionmodel
AT hamsamthabet usinggeneticalgorithminoutlierdetectionforregressionmodel
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