Abstract
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 language | English |
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Pages (from-to) | 417-426 |
Number of pages | 10 |
Journal | Big Data |
Volume | 9 |
Issue number | 6 |
DOIs | |
State | Published - 1 Dec 2021 |
Keywords
- Jaynes
- extreme rare events
- feature engineering
- inference
- pulp-And-paper
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Information Systems and Management