Shifting artificial data to detect system failures

DOI10.1111/itor.12047zbMath1312.62128OpenAlexW2162695109MaRDI QIDQ5252219

Jong-Seok Lee, Wook Yeon Hwang

Publication date: 29 May 2015

Published in: International Transactions in Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/itor.12047

zbMATH Keywords

random forestsmultivariate statistical process controlfailure detectionimbalanced classificationartificial contrasts

Mathematics Subject Classification ID

Reliability, availability, maintenance, inspection in operations research (90B25) Reasoning under uncertainty in the context of artificial intelligence (68T37) Reliability and life testing (62N05)

Related Items (3)

A distance-based control chart for monitoring multivariate processes using support vector machines ⋮ Variable selection for collaborative filtering with market basket data ⋮ Estimation on reliability models of bearing failure data

Cites Work

• Unnamed Item
• On the mathematical foundations of learning
• Robust kernel distance multivariate control chart using support vector principles
• One-Class Classification-Based Control Charts for Monitoring Autocorrelated Multivariate Processes
• Statistical process control for monitoring scheduling performance—addressing the problem of correlated data
• Multi-criteria Classification Methods in Financial and Banking Decisions
• Supervised learning for change-point detection
• Tuned artificial contrasts to detect signals
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests

This page was built for publication: Shifting artificial data to detect system failures