A survey on data‐efficient algorithms in big data era
A Adadi - Journal of Big Data, 2021 - Springer
The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …
many application domains do not have access to big data because acquiring data involves a …
A review of machine learning in processing remote sensing data for mineral exploration
The decline of the number of newly discovered mineral deposits and increase in demand for
different minerals in recent years has led exploration geologists to look for more efficient and …
different minerals in recent years has led exploration geologists to look for more efficient and …
Image synthesis with adversarial networks: A comprehensive survey and case studies
Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …
various application domains such as computer vision, medicine, and natural language …
Imgagn: Imbalanced network embedding via generative adversarial graph networks
Imbalanced classification on graphs is ubiquitous yet challenging in many real-world
applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) …
applications, such as fraudulent node detection. Recently, graph neural networks (GNNs) …
Oversampling adversarial network for class-imbalanced fault diagnosis
The collected data from industrial machines are often imbalanced, which poses a negative
effect on learning algorithms. However, this problem becomes more challenging for a mixed …
effect on learning algorithms. However, this problem becomes more challenging for a mixed …
Continuous sign language recognition through a context-aware generative adversarial network
I Papastratis, K Dimitropoulos, P Daras - Sensors, 2021 - mdpi.com
Continuous sign language recognition is a weakly supervised task dealing with the
identification of continuous sign gestures from video sequences, without any prior …
identification of continuous sign gestures from video sequences, without any prior …
On supervised class-imbalanced learning: An updated perspective and some key challenges
S Das, SS Mullick, I Zelinka - IEEE Transactions on Artificial …, 2022 - ieeexplore.ieee.org
The problem of class imbalance has always been considered as a significant challenge to
traditional machine learning and the emerging deep learning research communities. A …
traditional machine learning and the emerging deep learning research communities. A …
INS-GNN: Improving graph imbalance learning with self-supervision
Abstract Graph Neural Networks (GNNs) have achieved tremendous success in various
applications, such as node classification, link prediction and graph classification. However …
applications, such as node classification, link prediction and graph classification. However …
Two density-based sampling approaches for imbalanced and overlapping data
S Mayabadi, H Saadatfar - Knowledge-Based Systems, 2022 - Elsevier
An imbalanced dataset consists of a majority class and a minority class, where the former's
sample size is substantially larger than other classes. This difference disrupts the data …
sample size is substantially larger than other classes. This difference disrupts the data …
A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings
Abstract Unfortunately, Coronavirus disease 2019 (COVID-19) is spreading rapidly all over
the world. Along with causing many deaths, it has substantially affected the social life …
the world. Along with causing many deaths, it has substantially affected the social life …