Using Genetic Algorithm in Outlier Detection for Regression Model

Artículo

Using Genetic Algorithm in Outlier Detection for Regression Model

Zakariya Y. Algamal et al · University of Mosul, College of Education for Pure Science · 2018

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Author / responsible party

Zakariya Y. Algamal et al

Publisher

University of Mosul, College of Education for Pure Science

Year

2018

ISSN

1812-125X

ISSN

1812-125X

Language

English

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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.

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APA 7

al, Z. Y. A. E. (2018). Using Genetic Algorithm in Outlier Detection for Regression Model. https://doi.org/10.33899/edusj.2018.159314

MLA

al, Zakariya Y. Algamal et. "Using Genetic Algorithm in Outlier Detection for Regression Model." 2018. https://doi.org/10.33899/edusj.2018.159314.

Chicago

al, Zakariya Y. Algamal et. 2018. "Using Genetic Algorithm in Outlier Detection for Regression Model.". https://doi.org/10.33899/edusj.2018.159314.

Harvard

al, Z. Y. A. E. 2018, Using Genetic Algorithm in Outlier Detection for Regression Model, University of Mosul, College of Education for Pure Science, available at: https://doi.org/10.33899/edusj.2018.159314 [Accessed 10 Aug. 2026].

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Title: Using Genetic Algorithm in Outlier Detection for Regression Model

Author / contributors: Zakariya Y. Algamal et al

Publisher: University of Mosul, College of Education for Pure Science

Publication year: 2018

ISSN: 1812-125X

ISSN: 1812-125X

Language: English

Subjects

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outliers king; swamping; genetic algorithm; information criteria