Comparison of Quadratic Discrimination Analysis with Robust Quadratic Discrimination

Abstract

This study compared the performance of quadratic discrimination analysis and robust quadratic discrimination analysis using the Iris dataset from Kaggle. The robust quadratic discriminant analysis, designed to handle outliers and non-normal distributions, shows better performance with an Apparent Error Rate (APER) of 2.5%. In contrast, the quadratic discriminant analysis, used for data with multivariate normal distribution and different variance-covariance matrices among groups, yields an APER of 3.03%. These results indicate that robust quadratic discriminant analysis is more accurate in classification on this dataset compared to quadratic discriminant analysis.

Keywords: Apparent Error Rate, Quadratic Discrimination Analysis, Robust Quadratic Discrimination Analysis