Weighted logistic regression for large-scale imbalanced and rare events data
Latest developments in computing and technology, along with the availability of large amounts of raw data, have led to the development of many computational techniques and algorithms. Concerning binary data classification in particular, analysis of data containing rare events or disproportionate class distributions poses a great challenge to industry and to the machine learning community. Logistic Regression (LR) is a powerful classifier. The combination of LR and the truncated-regularized iteratively re-weighted least squares (TR-IRLS) algorithm, has provided a powerful classification method for large data sets. This study examines imbalanced data with binary response variables containing many more non-events (zeros) than events (ones). It has been established in the literature that these variables are difficult to predict and explain. This research combines rare events corrections to LR with truncated Newton methods. The proposed method, Rare Event Weighted Logistic Regression (RE-WLR), is capable of processing large imbalanced data sets at relatively the same processing speed as the TR-IRLS, however, with higher accuracy.
logistic regression classification truncated newton endogenous sampling kernel methods