Machine-learning-based text classification is one of the leading research areas and has a
wide range of applications, which include spam detection, hate speech identification …

Text data augmentation is an effective strategy for overcoming the challenge of limited
sample sizes in many natural language processing (NLP) tasks. This challenge is especially …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …

Text augmentation is a popular technique in natural language processing (NLP) that has
been shown to improve the performance of various downstream tasks. The goal of text …

Graph (structure) augmentation aims to perturb the graph structure through heuristic or
probabilistic rules, enabling the nodes to capture richer contextual information and thus …

Transfer-based attack adopts the adversarial examples generated on the surrogate model to
attack various models, making it applicable in the physical world and attracting increasing …

In many cases of machine learning, research suggests that the development of training data
might have a higher relevance than the choice and modelling of classifiers themselves …

Data Augmentation (DA) methods–a family of techniques designed for synthetic generation
of training data–have shown remarkable results in various Deep Learning and Machine …

Recently, deep learning models have made great breakthroughs in the field of computer
vision, relying on large-scale class-balanced datasets. However, most of them do not …