The class imbalance problem is one of the main challenges that hinders classifiers from
identifying unknown instances. When class distribution imbalance and class overlap coexist …

Collaborative representation-based classification (CRC) has been extensively applied to
various recognition fields due to its effectiveness and efficiency. Nevertheless, it is generally …

Nowadays, aerial vehicle swarm (AVS) formations have been widely applied to military
actions. Meanwhile, assessing their efficacy has also received increasing attention due to …

The continuous malicious software (malware) attacks on smartphones pose a serious threat
to the security of users, especially the dominant platform Android. Data-driven methods …

This paper proposes a novel data augmentation scheme called the conditional generative
adversarial network minority-class-augmented oversampling scheme (CTGAN-MOS) for …

By dint of the advantage of deeply integrating empirical knowledge and monitoring data, the
belief rule base (BRB) is widely used to assess the performance of complex systems …

Software fault prediction (SFP) aims to detect fault-prone software modules, which is
beneficial for allocating software testing resources and improving software quality. Recently …

Data imbalance is the main factor causing inaccurate classification results. Data-level
methods have achieved certain advantages in dealing with data imbalance problems, but …

For imbalanced classification problem, algorithm-level methods can effectively avoid the
information loss and noise introduction of data-level methods. However, the differences in …

Data-level methods often suffer from mode collapse when the minority class has multiple
distribution patterns. Some studies have tried addressing the problem using similarity …