  This is a machine learning application paper involving big data. We present
high-accuracy prediction methods of rare events in semi-structured machine log
files, which are produced at high velocity and high volume by NORC's
computer-assisted telephone interviewing (CATI) network for conducting surveys.
We judiciously apply natural language processing (NLP) techniques and
data-mining strategies to train effective learning and prediction models for
classifying uncommon error messages in the log---without access to source code,
updated documentation or dictionaries. In particular, our simple but effective
approach of features preallocation for learning from imbalanced data coupled
with naive Bayes classifiers can be conceivably generalized to supervised or
semi-supervised learning and prediction methods for other critical events such
as cyberattack detection.
