Extreme Rare Events Identification through Jaynes Inferential Approach

Yair Neuman, Yochai Cohen, Eden Erez

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)417-426
Number of pages10
JournalBig Data
Issue number6
StatePublished - 1 Dec 2021


  • Jaynes
  • extreme rare events
  • feature engineering
  • inference
  • pulp-And-paper

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Information Systems and Management


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