Logistic regression in large rare events and imbalanced data: A performance comparison of prior correction and weighting methods

The purpose of this study is to use the truncated Newton method in prior correction logistic regression (LR). A regularization term is added to prior correction LR to improve its performance, which results in the truncated-regularized prior correction algorithm. The performance of this algorithm is compared with that of weighted LR and the regular LR methods for large imbalanced binary class data sets. The results, based on the KDD99 intrusion detection data set, and 6 other data sets at both the prior correction and the weighted LRs have the same computational efficiency when the truncated Newton method is used in both of them. A higher discriminative performance, however, resulted from weighting, which exceeded both the prior correction and the regular LR on nearly all the data sets. From this study, we conclude that weighting outperforms both the regular and prior correction LR models in most data sets and it is the method of choice when LR is used to evaluate imbalanced and rare event data.