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...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Education for Pure Sciences
2018-06-01
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| Series: | مجلة التربية والعلم |
| Subjects: | |
| Online Access: | https://edusj.mosuljournals.com/article_159314_39b5268029f82b3c47f9622965a2dfab.pdf |
| _version_ | 1818541919906562048 |
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| author | Zakariya Y. Algamal Hamsa M.Thabet |
| author_facet | Zakariya Y. Algamal Hamsa M.Thabet |
| author_sort | Zakariya Y. Algamal |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-11T22:15:26Z |
| format | Article |
| id | doaj.art-6e7713bc8ed145b8a8b12091c1ad003f |
| institution | Directory Open Access Journal |
| issn | 1812-125X 2664-2530 |
| language | Arabic |
| last_indexed | 2024-12-11T22:15:26Z |
| publishDate | 2018-06-01 |
| publisher | College of Education for Pure Sciences |
| record_format | Article |
| series | مجلة التربية والعلم |
| spelling | doaj.art-6e7713bc8ed145b8a8b12091c1ad003f2022-12-22T00:48:37ZaraCollege 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 |
| 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 |
| title | 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_short | Using Genetic Algorithm in Outlier Detection for Regression Model |
| title_sort | using genetic algorithm in outlier detection for regression model |
| 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 |