Deep learning based on Transformer architecture for power system short-term voltage stability assessment with class imbalance
Most existing data-driven power system short-term voltage stability assessment (STVSA)
approaches presume class-balanced input data. However, in practical applications, the …
approaches presume class-balanced input data. However, in practical applications, the …
Semisupervised change detection using graph convolutional network
Most change detection (CD) methods are unsupervised as collecting substantial
multitemporal training data is challenging. Unsupervised CD methods are driven by …
multitemporal training data is challenging. Unsupervised CD methods are driven by …
Pairwise constraints-based semi-supervised fuzzy clustering with multi-manifold regularization
Y Wang, L Chen, J Zhou, T Li, Y Yu - Information Sciences, 2023 - Elsevier
Introducing a handful of pairwise constraints into fuzzy clustering models to revise
memberships has been proven beneficial to boosting clustering performance. However …
memberships has been proven beneficial to boosting clustering performance. However …
SMKFC-ER: Semi-supervised multiple kernel fuzzy clustering based on entropy and relative entropy
The present study aimed to present a new algorithm called Semi-supervised Multiple Kernel
Fuzzy Clustering based on Entropy and Relative entropy (SMKFC-ER) by focusing on …
Fuzzy Clustering based on Entropy and Relative entropy (SMKFC-ER) by focusing on …
A hybrid interval type-2 semi-supervised possibilistic fuzzy c-means clustering and particle swarm optimization for satellite image analysis
Although satellite images can provide more information about the earth's surface in a
relatively short time and over a large scale, they are affected by observation conditions and …
relatively short time and over a large scale, they are affected by observation conditions and …
[HTML][HTML] Change detection in remote sensing images based on image mapping and a deep capsule network
Homogeneous image change detection research has been well developed, and many
methods have been proposed. However, change detection between heterogeneous images …
methods have been proposed. However, change detection between heterogeneous images …
Semi-supervised deep fuzzy C-mean clustering for imbalanced multi-class classification
Semi-supervised learning has been successfully connected in the research fields of
machine learning such as data mining and dynamic data analysis. Imbalance class learning …
machine learning such as data mining and dynamic data analysis. Imbalance class learning …
A new approach for semi-supervised fuzzy clustering with multiple fuzzifiers
Data clustering is the process of dividing data elements into different clusters in which
elements in one cluster have more similarity than those in other clusters. Semi-supervised …
elements in one cluster have more similarity than those in other clusters. Semi-supervised …
Adaptive safety-aware semi-supervised clustering
Recently, safe semi-supervised clustering (S3C) has become an emerging topic in machine
learning field. S3C aims to reduce the performance degradation probability of wrong prior …
learning field. S3C aims to reduce the performance degradation probability of wrong prior …
SDCDNet: A semi-dual change detection network framework with super-weak label for remote sensing image
Most current change detection methods require a large amount of labeled data to train huge
parameters. To break this limitation, this article proposes a novel semi-supervised learning …
parameters. To break this limitation, this article proposes a novel semi-supervised learning …