A literature review on one-class classification and its potential applications in big data
In severely imbalanced datasets, using traditional binary or multi-class classification typically
leads to bias towards the class (es) with the much larger number of instances. Under such …
leads to bias towards the class (es) with the much larger number of instances. Under such …
Multilayer one-class extreme learning machine
One-class classification has been found attractive in many applications for its effectiveness
in anomaly or outlier detection. Representative one-class classification algorithms include …
in anomaly or outlier detection. Representative one-class classification algorithms include …
Nuclear Power Plant accident identification system with “don't know” response capability: Novel deep learning-based approaches
VHC Pinheiro, MC dos Santos, FSM do Desterro… - Annals of nuclear …, 2020 - Elsevier
When it comes to Nuclear Power Plants (NPP) operation, one of the major challenges in the
Human Factors Engineering area is to provide reliable support systems, capable of rapidly …
Human Factors Engineering area is to provide reliable support systems, capable of rapidly …
Deep autoencoder for false positive reduction in handgun detection
In an object detection system, the main objective during training is to maintain the detection
and false positive rates under acceptable levels when the model is run over the test set …
and false positive rates under acceptable levels when the model is run over the test set …
A multiple-architecture deep learning approach for nuclear power plants accidents classification including anomaly detection and “don't know” response
Nuclear power plants (NPPs) are complex systems that are monitored by a team of highly
trained operators, that in case of an anomalous event on the NPP, such as an accident, must …
trained operators, that in case of an anomalous event on the NPP, such as an accident, must …
Elastic-net based robust extreme learning machine for one-class classification
W Zhan, K Wang, J Cao - Signal Processing, 2023 - Elsevier
The one-class extreme learning machine (OC-ELM) builds a classification model by learning
samples of the known class to detect abnormal samples, and has the advantages of high …
samples of the known class to detect abnormal samples, and has the advantages of high …
Embedded stacked group sparse autoencoder ensemble with L1 regularization and manifold reduction
Y Li, Y Lei, P Wang, M Jiang, Y Liu - Applied Soft Computing, 2021 - Elsevier
Learning useful representations from original features is a key issue in classification tasks.
Stacked autoencoders (SAEs) are easy to understand and realize, and they are powerful …
Stacked autoencoders (SAEs) are easy to understand and realize, and they are powerful …
Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems
F Arellano-Espitia, M Delgado-Prieto… - Sensors, 2021 - mdpi.com
The rapid growth in the industrial sector has required the development of more productive
and reliable machinery, and therefore, leads to complex systems. In this regard, the …
and reliable machinery, and therefore, leads to complex systems. In this regard, the …
Deep metric learning for open-set human action recognition in videos
Human action recognition (HAR) is a topic widely studied in computer vision and pattern
recognition. Despite the success of recent models for this issue, most of them approach HAR …
recognition. Despite the success of recent models for this issue, most of them approach HAR …
Representation learning for continuous action spaces is beneficial for efficient policy learning
Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional
reinforcement learning (RL) with the help of the perception capability of deep learning and …
reinforcement learning (RL) with the help of the perception capability of deep learning and …