Weighted logistic regression for large-scale imbalanced and rare events data

Research output: Contribution to conferencePaperpeer-review

2 Scopus citations


Logistic regression (LR) is a powerful classifier. The combination of LR and the truncated-regularized iteratively re-weighted least squares (TR-IRLS) algorithm, has led to 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.

Original languageBritish English
Number of pages7
StatePublished - 2013
EventIIE Annual Conference and Expo 2013 - San Juan, Puerto Rico
Duration: 18 May 201322 May 2013


ConferenceIIE Annual Conference and Expo 2013
Country/TerritoryPuerto Rico
CitySan Juan


  • Classification endogenous sampling logistic regression truncated Newton


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