Abstract Multi-Label Classification (MLC) is an extension of the standard single-label
classification where each data instance is associated with several labels simultaneously …

The research on intelligent fault diagnosis has yielded remarkable achievements based on
artificial intelligence-related technologies. In engineering scenarios, machines usually work …

We study why overparameterization—increasing model size well beyond the point of zero
training error—can hurt test error on minority groups despite improving average test error …

In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Context. Electroencephalography (EEG) is a complex signal and can require several years
of training, as well as advanced signal processing and feature extraction methodologies to …

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening
are being adopted worldwide by medical institutions. In such a context, generating fair and …

Educational data mining is capable of producing useful data-driven applications (eg, early
warning systems in schools or the prediction of students' academic achievement) based on …

Attention Mechanism has become very popular in the field of mechanical fault diagnosis in
recent years and has become an important technique for scholars to study and apply. The …

Medical datasets are usually imbalanced, where negative cases severely outnumber
positive cases. Therefore, it is essential to deal with this data skew problem when training …

The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …