Twin support vector machine (TWSVM) and twin support vector regression (TSVR) are newly
emerging efficient machine learning techniques which offer promising solutions for …

Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Despite over two decades of progress, imbalanced data is still considered a significant
challenge for contemporary machine learning models. Modern advances in deep learning …

Deep learning can be applied to the field of fault diagnosis for its powerful feature
representation capabilities. When a certain class fault samples available are very limited, it …

Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

Real-time prediction of the rock mass class in front of the tunnel face is essential for the
adaptive adjustment of tunnel boring machines (TBMs). During the TBM tunnelling process …

This study aims to establish a novel entropy-based sparsity measure for two main purposes:
first is for the prognosis of bearing defects, secondly it is employed to construct sparsogram …

Heart disease is the deadliest disease and one of leading causes of death worldwide.
Machine learning is playing an essential role in the medical side. In this paper, ensemble …

In the past few decades, credit scoring has become an increasing concern for financial
institutions and is currently a popular topic of research. This study aims to generate a novel …

In many real applications of planetary gearbox fault diagnosis, the number of fault samples
is much less than normal samples while fault samples are hard to collected in different …