The class imbalance problem is a challenging problem in the data mining area. To
overcome the low classification performance related to imbalanced datasets, sampling …

A rapid and effective method for detecting fungal infection in choy sum seeds is necessary to
ensure good yield. In this study, 127 spectra of healthy choy sum seeds and 1479 spectra of …

For imbalanced binary classification, most methods classify data in the original space by
balancing data or modifying classification algorithms. However, for datasets with severe …

A stacked autoencoder (SAE) is a widely used deep network. However, existing deep SAEs
focus on original samples without considering the hierarchical structural information …

The use of neural network models to monitor bearing vibration signals can easily be affected
by noise, which leads to a decrease in the model test accuracy. Therefore, the existence of …

Stack autoencoder (SAE), as a representative deep network, has unique and excellent
performance in feature learning, and has received extensive attention from researchers …

In data mining, deep learning and machine learning models face class imbalance problems,
which result in a lower detection rate for minority class samples. An improved Synthetic …

Machine learning algorithm extends its horizon in various application domains. Real time
applications in various sectors which can be effectively handled with machine learning …

Class imbalance is not new in the world of Machine Learning (ML). In this digital world,
almost all physical systems are converted to automated systems. Digitization of systems …

In this digital world, sharing of information is very easy and cost-effective; resulting in a large
amount of high-dimensional data, available in a variety of domains such as healthcare …