Comparison of Linear Discriminant Analysis and Logistic Regression for Data Classification

Linear discriminant analysis (LDA) and logistic regression (LR) are often used for the purpose of classifying populations or groups using a set of predictor variables. Assumptions of multivariate normality and equal variance-covariance matrices across groups are required before proceeding with LDA, but such assumptions are not required for LR and hence LR is considered to be much more robust than LDA. In this paper, several real datasets which are different in terms of normality, number of independent variables and sample size are used to study the performance of both methods. The methods are compared based on the percentage of correct classification and B index. The results show that overall, LR performs better regardless of the distribution of the data is normal or nonnormal. However, LR needs longer computing time than LDA with the increase in sample size. The performance of LDA was also tested by using various prior probabilities. The results show that the average percentage of correct classification and the B index are higher when the prior probability is set based on the group size rather than using equal probabilities for all groups.