The identification of extreme rare events is a challenge that appears in several real-world contexts, from screening for solo perpetrators to the prediction of failures in industrial production. In this article, we explain the challenge and present a new methodology for addressing it, a methodology that may be considered in terms of features engineering. This methodology, which is based on Jaynes inferential approach, is tested on a dataset dealing with failures in production in the pulp-and-paper industry. The results are discussed in the context of the benefits of using the approach for features engineering in practical contexts involving measurable risks.
Keywords: Jaynes; extreme rare events; feature engineering; inference; pulp-and-paper.