This paper provides an extensive and in-depth survey of behavior modeling for complex
intelligent systems, focusing specifically on the innovative applications of Generative …

Learning class-imbalance data has become a challenging task in machine learning.
Oversampling is an effective way to achieve rebalancing between classes by generating …

Data-driven intelligent fault root cause location is important to the reliability and safety of
network operation and maintenance. However, the number of fault samples is much greater …

Software defect prediction technology is used to assist software practitioners in effectively
allocating test resources and identifying hidden defects in a timely manner. However, the …

An accurate prediction of bugs in software projects can help in improving software projects'
quality. A simple majority voting (SMV) ensemble is an effective technique for bug prediction …

Software defect prediction is an active research area. Researchers have proposed many
approaches to overcome the imbalanced defect problem and build highly effective machine …

Defects cause a great impact on software reliability, and therefore also reduce software
quality. To address this, the area of software defect prediction has emerged. This is an area …

The wrong line-transformer relationship is one of the main reasons that leads to the failure of
the line loss assessment of the distribution network with voltage levels of 10 kV and below …

In response to the need to generate synthetic minority class samples to extend minority
classes, the SMOTE-based oversampling methods have been favored for class-imbalanced …

The study of machine learning has revealed that it can unleash new applications in a variety
of disciplines. Many limitations limit their expressiveness, and researchers are working to …