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Extreme Rare Events Identification Through Jaynes Inferential Approach
- Source :
- Big Data
- Publication Year :
- 2021
-
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.
- Subjects :
- Feature engineering
inference
Information Systems and Management
extreme rare events
Computer science
business.industry
Industrial production
Inference
macromolecular substances
Original Articles
Machine learning
computer.software_genre
Jaynes
Computer Science Applications
feature engineering
Rare events
Industry
Identification (biology)
Artificial intelligence
business
computer
pulp-and-paper
Information Systems
Subjects
Details
- ISSN :
- 2167647X
- Volume :
- 9
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- Big data
- Accession number :
- edsair.doi.dedup.....473566d36a77b8a924ddebab396c4316