4.6 Article

Learning With Imbalanced Data in Smart Manufacturing: A Comparative Analysis

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Automation & Control Systems

Multisubspace Orthogonal Canonical Correlation Analysis for Quality-Related Plant-Wide Process Monitoring

Bing Song et al.

Summary: The article introduces a novel data-driven method called multisubspace orthogonal canonical correlation analysis, which can real-time judge whether faults affect product quality. By dividing the process variable space into four subspaces, conducting orthogonal CCA for feature extraction and monitoring statistics, the method is developed and tested successfully.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

Article Computer Science, Information Systems

Kernel density estimation based sampling for imbalanced class distribution

Firuz Kamalov

INFORMATION SCIENCES (2020)

Article Computer Science, Artificial Intelligence

Hybrid Classifier Ensemble for Imbalanced Data

Kaixiang Yang et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2020)

Article Operations Research & Management Science

Distributionally robust optimization with decision dependent ambiguity sets

Fengqiao Luo et al.

OPTIMIZATION LETTERS (2020)

Article Computer Science, Artificial Intelligence

Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost

Chen Wang et al.

PATTERN RECOGNITION LETTERS (2020)

Article Automation & Control Systems

Multisubspace Elastic Network for Multimode Quality-Related Process Monitoring

Bing Song et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

Article Computer Science, Information Systems

Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets

Christos K. Aridas et al.

IEEE ACCESS (2020)

Article Automation & Control Systems

Performance-Indicator-Oriented Concurrent Subspace Process Monitoring Method

Bing Song et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2019)

Article Computer Science, Artificial Intelligence

Oversampling method using outlier detectable generative adversarial network

Joo-Hyuk Oh et al.

EXPERT SYSTEMS WITH APPLICATIONS (2019)

Article Computer Science, Artificial Intelligence

His-GAN: A histogram-based GAN model to improve data generation quality

Wei Li et al.

NEURAL NETWORKS (2019)

Article Computer Science, Artificial Intelligence

A generative neural network model for the quality prediction of work in progress products

Guodong Wang et al.

APPLIED SOFT COMPUTING (2019)

Article Computer Science, Information Systems

Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data

Ke Cheng et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

A Methodology for Prognostics Under the Conditions of Limited Failure Data Availability

Gishan D. Ranasinghe et al.

IEEE ACCESS (2019)

Article Computer Science, Theory & Methods

Survey on deep learning with class imbalance

Justin M. Johnson et al.

JOURNAL OF BIG DATA (2019)

Article Computer Science, Theory & Methods

Large-Scale Indexing, Discovery, and Ranking for the Internet of Things (IoT)

Yasmin Fathy et al.

ACM COMPUTING SURVEYS (2018)

Article Geosciences, Multidisciplinary

Dissimilarity measures in detrital geochronology

Pieter Vermeesch

EARTH-SCIENCE REVIEWS (2018)

Article Engineering, Electrical & Electronic

Improved Wasserstein conditional generative adversarial network speech enhancement

Shan Qin et al.

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2018)

Article Computer Science, Information Systems

Cost-sensitive and hybrid-attribute measure multi-decision tree over imbalanced data sets

Fenglian Li et al.

INFORMATION SCIENCES (2018)

Article Computer Science, Artificial Intelligence

SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary

Alberto Fernandez et al.

JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH (2018)

Article Engineering, Industrial

Deep learning for smart manufacturing: Methods and applications

Jinjiang Wang et al.

JOURNAL OF MANUFACTURING SYSTEMS (2018)

Article Engineering, Industrial

Data-driven smart manufacturing

Fei Tao et al.

JOURNAL OF MANUFACTURING SYSTEMS (2018)

Article Computer Science, Artificial Intelligence

Auto-painter: Cartoon image generation from sketch by using conditional Wasserstein generative adversarial networks

Yifan Liu et al.

NEUROCOMPUTING (2018)

Proceedings Paper Computer Science, Artificial Intelligence

Imbalanced Data Classification Based on Hybrid Methods

Nai-Nan Zhang et al.

PROCEEDINGS OF THE 2018 2ND INTERNATIONAL CONFERENCE ON BIG DATA RESEARCH (ICBDR 2018) (2018)

Article Physics, Multidisciplinary

On Wasserstein Two-Sample Testing and Related Families of Nonparametric Tests

Aaditya Ramdas et al.

ENTROPY (2017)

Article Computer Science, Information Systems

Clustering-based undersampling in class-imbalanced data

Wei-Chao Lin et al.

INFORMATION SCIENCES (2017)

Editorial Material Biochemical Research Methods

POINTS OF SIGNIFICANCE Principal component analysis

Jake Lever et al.

NATURE METHODS (2017)

Article Transportation Science & Technology

Planning Flexible Maintenance for Heavy Trucks using Machine Learning Models, Constraint Programming, and Route Optimization

Jonas Biteus et al.

SAE INTERNATIONAL JOURNAL OF MATERIALS AND MANUFACTURING (2017)

Review Computer Science, Artificial Intelligence

An insight into imbalanced Big Data classification: outcomes and challenges

Alberto Fernandez et al.

COMPLEX & INTELLIGENT SYSTEMS (2017)

Proceedings Paper Computer Science, Artificial Intelligence

CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

Jianmin Bao et al.

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV) (2017)

Review Computer Science, Artificial Intelligence

Learning from imbalanced data: open challenges and future directions

Bartosz Krawczyk

PROGRESS IN ARTIFICIAL INTELLIGENCE (2016)

Proceedings Paper Computer Science, Artificial Intelligence

Prediction of Failures in the Air Pressure System of Scania Trucks Using a Random Forest and Feature Engineering

Christopher Gondek et al.

ADVANCES IN INTELLIGENT DATA ANALYSIS XV (2016)

Proceedings Paper Computer Science, Artificial Intelligence

IDA 2016 Industrial Challenge: Using Machine Learning for Predicting Failures

Camila Ferreira Costa et al.

ADVANCES IN INTELLIGENT DATA ANALYSIS XV (2016)

Article Computer Science, Artificial Intelligence

On the usefulness of one-class classifier ensembles for decomposition of multi-class problems

Bartosz Krawczyk et al.

PATTERN RECOGNITION (2015)

Article Computer Science, Artificial Intelligence

A survey of multiple classifier systems as hybrid systems

Michal Wozniak et al.

INFORMATION FUSION (2014)

Review Computer Science, Artificial Intelligence

Ensemble-based classifiers

Lior Rokach

ARTIFICIAL INTELLIGENCE REVIEW (2010)

Article Computer Science, Artificial Intelligence

A scalable intelligent non-content-based spam-filtering framework

Yong Hu et al.

EXPERT SYSTEMS WITH APPLICATIONS (2010)

Review Computer Science, Artificial Intelligence

Learning from Imbalanced Data

Haibo He et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2009)

Article Computer Science, Artificial Intelligence

Random forests

L Breiman

MACHINE LEARNING (2001)

Article Statistics & Probability

Greedy function approximation: A gradient boosting machine

JH Friedman

ANNALS OF STATISTICS (2001)