In the last two decades, the fast and elitist non-dominated sorting genetic algorithm (NSGA-
II) has attracted extensive research interests, and it is still one of the hottest research …

The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

The combined impact of new computing resources and techniques with an increasing
avalanche of large datasets, is transforming many research areas and may lead to …

Learning from class-imbalanced data continues to be a common and challenging problem in
supervised learning as standard classification algorithms are designed to handle balanced …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Dataset scaling, also known as normalization, is an essential preprocessing step in a
machine learning pipeline. It is aimed at adjusting attributes scales in a way that they all vary …

Safety is one of the key requirements for automated vehicles and fault diagnosis is an
effective technique to enhance the vehicle safety. The model-based fault diagnosis method …

In brain storm optimization (BSO), the convergent operation utilizes a clustering strategy to
group the population into multiple clusters, and the divergent operation uses this cluster …

Multiple instance learning (MIL) is a form of weakly supervised learning where training
instances are arranged in sets, called bags, and a label is provided for the entire bag. This …