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

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

Deep learning has achieved remarkable results in many computer vision tasks. Deep neural
networks typically rely on large amounts of training data to avoid overfitting. However …

Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …

Data imbalance in Machine Learning refers to an unequal distribution of classes within a
dataset. This issue is encountered mostly in classification tasks in which the distribution of …

Credit card fraud is becoming a serious and growing problem as a result of the emergence
of innovative technologies and communication methods, such as contactless payment. In …

Abstract The advent of Big Data has ushered a new era of scientific breakthroughs. One of
the common issues that affects raw data is class imbalance problem which refers to …

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 …

In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …