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Process-monitoring-for-quality — A machine learning-based modeling for rare event detection
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-57868-4_57openalex-snapshot
Estimating attributes: Analysis and extensions of RELIEF
inferredcites →https://doi.org/10.1007/978-981-287-411-5openalex-snapshot
Grammar-Based Feature Generation for Time-Series Prediction
inferredcites →https://doi.org/10.1016/j.cie.2017.01.028openalex-snapshot
Mining the relationship between production and customer service data for failure analysis of industrial products
inferredcites →https://doi.org/10.1016/j.cie.2019.01.046openalex-snapshot
Reliability-oriented design of integrated model of preventive maintenance and quality control policy with time-between-events control chart
inferredcites →https://doi.org/10.1016/j.compeleceng.2013.11.024openalex-snapshot
A survey on feature selection methods
inferredcites →https://doi.org/10.1016/j.enbuild.2015.11.071openalex-snapshot
Accurate occupancy detection of an office room from light, temperature, humidity and CO 2 measurements using statistical learning models
inferredcites →https://doi.org/10.1016/j.eswa.2016.12.035openalex-snapshot
Learning from class-imbalanced data: Review of methods and applications
inferredcites →https://doi.org/10.1016/j.jmsy.2013.05.006openalex-snapshot
Feature selection for manufacturing process monitoring using cross-validation
inferredcites →https://doi.org/10.1016/j.mfglet.2018.01.001openalex-snapshot
Process-monitoring-for-quality — A model selection criterion
inferredcites →https://doi.org/10.1016/j.neunet.2019.08.018openalex-snapshot
Online sequential class-specific extreme learning machine for binary imbalanced learning
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
inferredcites →https://doi.org/10.1016/j.promfg.2019.06.166openalex-snapshot
Process-Monitoring-for-Quality — A Model Selection Criterion for l - Regularized Logistic Regression
inferredcites →https://doi.org/10.1023/a:1025667309714openalex-snapshot
Theoretical and Empirical Analysis of ReliefF and RReliefF
inferredcites →https://doi.org/10.1080/21693277.2016.1192517openalex-snapshot
Machine learning in manufacturing: advantages, challenges, and applications
inferredcites →https://doi.org/10.1109/icsess.2014.6933569openalex-snapshot
Feature selection using feature ranking, correlation analysis and chaotic binary particle swarm optimization
inferredcites →https://doi.org/10.1109/ijcnn.2012.6252640openalex-snapshot
Feature selection via regularized trees
inferredcites →https://doi.org/10.1109/tpel.2016.2608842openalex-snapshot
A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems
inferredcites →https://doi.org/10.1109/tsmca.2009.2029559openalex-snapshot
RUSBoost: A Hybrid Approach to Alleviating Class Imbalance
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02080.xopenalex-snapshot
Regression Shrinkage and Selection Via the Lasso
inferredcites →https://doi.org/10.1115/1.4036833openalex-snapshot
Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy
inferredcites →https://doi.org/10.1142/s0218539316400027openalex-snapshot
Two-Dimensional Multi-Release Software Reliability Modeling for Fault Detection and Fault Correction Processes
inferredcites →https://doi.org/10.1145/1015330.1015435openalex-snapshot
Feature selection, L 1 vs. L 2 regularization, and rotational invariance
inferredcites →https://doi.org/10.1177/1687814018755519openalex-snapshot
Machine learning techniques for quality control in high conformance manufacturing environment
inferredcites →https://doi.org/10.1198/tech.2004.s245openalex-snapshot
Probability and Statistics for Engineering and the Sciences
inferredcites →https://doi.org/10.1201/9781003369356-8openalex-snapshot
Explainable Artificial Intelligence (XAI)
inferredcites →https://doi.org/10.1214/09-ss054openalex-snapshot
A survey of cross-validation procedures for model selection
inferredcites →https://doi.org/10.2307/2532427openalex-snapshot
Probability and Statistics for Engineering and the Sciences.
inferredcites →https://doi.org/10.2307/3802723openalex-snapshot
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
inferredcites →https://doi.org/10.48550/arxiv.1502.02127openalex-snapshot
Hyperparameter Search in Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.1602.04938openalex-snapshot
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4658137openalex-extracted
A Systematic Review of Rare Events Detection Using Machine Learning and Deep Learning
inferred← citeshttps://doi.org/10.1007/978-3-030-85577-2_31openalex-snapshot
Predictive Quality Defect Detection Using Machine Learning Algorithms: A Case Study from Automobile Industry
inferred← citeshttps://doi.org/10.1007/978-3-031-26490-0_5openalex-snapshot
Human-Machine-Interaction in Innovative Work Environment 4.0 – A Human-Centered Approach
inferred← citeshttps://doi.org/10.1007/978-3-658-40570-0_16openalex-snapshot
Kollaboration, künstliche Intelligenz und erweiterte Realität ermöglichen den Einkauf X.0
inferred← citeshttps://doi.org/10.1007/978-3-658-45670-2_16openalex-snapshot
Collaboration, Artificial Intelligence and Augmented Reality Enable Purchasing X.0
inferred← citeshttps://doi.org/10.1007/978-3-658-45899-7_3openalex-snapshot
Algorithmen und Anwendungsgebiete des maschinellen Lernens für die prädiktive Prozesskontrolle
inferred← citeshttps://doi.org/10.1016/j.cie.2025.111102openalex-snapshot
A novel deep learning approach to online quality control for multiple time series process
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2024.108633openalex-snapshot
Alarm rationalization and dynamic risk analyses for rare abnormal events
inferred← citeshttps://doi.org/10.1016/j.rinam.2026.100722openalex-snapshot
A robust logistic regression method for rare events with built-in uncertainty bands
inferred← citeshttps://doi.org/10.1080/00207543.2024.2382379openalex-snapshot
Data analytics in zero defect manufacturing: a systematic literature review and proposed framework
inferred← citeshttps://doi.org/10.1080/00224065.2025.2489926openalex-snapshot
Detection possibilities when count levels are low
inferred← citeshttps://doi.org/10.1080/08982112.2023.2206679openalex-snapshot
The decay of Six Sigma and the rise of Quality 4.0 in manufacturing innovation
inferred← citeshttps://doi.org/10.1080/0951192x.2022.2128219openalex-snapshot
Data analytics in quality 4.0: literature review and future research directions
inferred← citeshttps://doi.org/10.1109/access.2024.3382140openalex-snapshot
A Systematic Review of Rare Events Detection Across Modalities Using Machine Learning and Deep Learning
inferred← citeshttps://doi.org/10.17776/csj.1605507openalex-snapshot
Advanced Machine Learning Strategies for Effective Rare Event Classification: A Comparative Study
inferred← citeshttps://doi.org/10.3390/pr12081730openalex-snapshot
Capability Indices for Digitized Industries: A Review and Outlook of Machine Learning Applications for Predictive Process Control
inferred← citeshttps://doi.org/10.36227/techrxiv.24162678openalex-snapshot
A Systematic Review of Rare Events Detection Across Modalities using Machine Learning and Deep Learning
inferred← citeshttps://doi.org/10.4271/2020-01-1302openalex-snapshot
Process-Monitoring-for-Quality - A Step Forward in the Zero Defects Vision
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