In an actual industrial scenario, machines typically operate normally for the majority of the
time, with malfunctions occurring only occasionally. As a result, there is very little recorded …

The synthetic minority over-sampling technique (SMOTE) is a well-known over-sampling
method for handling imbalanced data. However, SMOTE and many of its variants are …

Several oversampling methods have been proposed for solving the class imbalance
problem. However, most of them require searching the k-nearest neighbors to generate …

Credit risk evaluation is a crucial yet challenging problem in financial analysis. It can not
only help institutions reduce risk and ensure profitability, but also improve consumers' fair …

With the advent of machine learning and its numerous techniques, many real-world
problems have been solved like credit card fraud detection, cancer susceptibility and …

The ship target recognition (STR) is greatly related to the battlefield situation awareness,
which has recently gained prominence in the military domains. With the diversification and …

Aiming at the low classification accuracy of unbalanced data sets, an improved SMOTE over-
sampling algorithm ACC-SMOTE (Ant Colony Clustering Synthetic Minority Oversampling …

Long-tailed classifications make it very challenging to deal with class-imbalanced problems
using deep convolutional neural networks (CNNs). Existing solutions based on re-balancing …

The samples representing abnormal situation is usually very few in the dataset, which
makes it difficult to learn the features of abnormal samples by machine-learning-based …

Cancer stands as the second leading global cause of mortality, with lung cancer emerging
as the foremost contributor to this statistic in the year 2020. Significant scientific …