- All Journals
- Al-Qadisiyah University
- Basrah University
- Kirkuk University
- Mosul University
- The Iraqi Borad for Medical Specialization
- University of Anbar
- University of Technology
- Al Mustafa University
- Al-Zahraa University for Women
- Alnoor University
- Kunooz University
- Misan University
- Ninevah University
- Tikrit University
- University of Al-Hamdaniya
- University of Kerbala
Using Genetic Algorithm in Outlier Detection for Regression Model | ||
| Journal of Education and Science | ||
| Article 9, Volume 27, Issue 3, 2018, Pages 136-142 PDF (441.39 K) | ||
| DOI: 10.33899/edusj.2018.159314 | ||
| Authors | ||
| Zakariya Y. Algamal; Hamsa M.Thabet | ||
| 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. | ||
| Keywords | ||
| outliers king; swamping; genetic algorithm; information criteria | ||
|
Statistics Article View: 394 PDF Download: 512 |
||