Using Genetic Algorithm in Outlier Detection for Regression Model

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.