Related references
Note: Only part of the references are listed.Appropriateness of performance indices for imbalanced data classification: An analysis
Sankha Subhra Mullick et al.
PATTERN RECOGNITION (2020)
Industry 4.0: A bibliometric analysis and detailed overview
Pranab K. Muhuri et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2019)
A fault detection system based on unsupervised techniques for industrial control loops
Esteban Jove et al.
EXPERT SYSTEMS (2019)
Recent Progress of Anomaly Detection
Xiaodan Xu et al.
COMPLEXITY (2019)
Learning in presence of class imbalance and class overlapping by using one-class SVM and undersampling technique
Debashree Devi et al.
CONNECTION SCIENCE (2019)
An Affordable Fast Early Warning System for Edge Computing in Assembly Line
Muhammad Syafrudin et al.
APPLIED SCIENCES-BASEL (2019)
Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection
Menghua Luo et al.
CMC-COMPUTERS MATERIALS & CONTINUA (2019)
On Fault Detection and Diagnosis in Robotic Systems
Eliahu Khalastchi et al.
ACM COMPUTING SURVEYS (2018)
Fault Detection in Wireless Sensor Networks Through SVM Classifier
Salah Zidi et al.
IEEE SENSORS JOURNAL (2018)
Handling data irregularities in classification: Foundations, trends, and future challenges
Swagatam Das et al.
PATTERN RECOGNITION (2018)
Classifying component failures of a hybrid electric vehicle fleet based on load spectrum data Balanced random forest approaches employing uni- and multivariate decision trees
Philipp Bergmeir et al.
NEURAL COMPUTING & APPLICATIONS (2016)
A systematic review of machine learning techniques for software fault prediction
Ruchika Malhotra
APPLIED SOFT COMPUTING (2015)
Dealing with the evaluation of supervised classification algorithms
Guzman Santafe et al.
ARTIFICIAL INTELLIGENCE REVIEW (2015)
Anomaly detection in dynamic networks: a survey
Stephen Ranshous et al.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS (2015)
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics
Victoria Lopez et al.
INFORMATION SCIENCES (2013)
Multi-sensor data fusion using support vector machine for motor fault detection
Tribeni Prasad Banerjee et al.
INFORMATION SCIENCES (2012)
Pattern classification with missing data: a review
Pedro J. Garcia-Laencina et al.
NEURAL COMPUTING & APPLICATIONS (2010)
Learning from Imbalanced Data
Haibo He et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2009)
Using AUC and accuracy in evaluating learning algorithms
J Huang et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2005)
Share Paper
