Recent developments in computing and technology, along with the availability of large amounts of raw data, have contributed to the creation of many effective techniques and algorithms in the fields of pattern recognition and machine learning. Some of the main objectives for developing these algorithms are to identify patterns within the available data or to make predictions, or both. Great success has been achieved with many classification techniques in real-life applications. 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. This study examines rare events (REs) with binary dependent variables containing many times more non-events (zeros) than events (ones). These variables are difficult to predict and to explain as has been demonstrated in the literature. This research combines rare events corrections on Logistic Regression (LR) with truncated-Newton methods and applies these techniques on Kernel Logistic Regression (KLR). The resulting model, Rare-Event Weighted Kernel Logistic Regression (RE-WKLR) is a combination of weighting, regularization, approximate numerical methods, kernelization, bias correction, and efficient implementation, all of which enable RE-WKLR to be at once fast, accurate, and robust
