Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets
with increased complexity, making feature selection an essential technique for single-cell …

The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …

The slime mould algorithm (SMA) is a logical swarm-based stochastic optimizer that is easy
to understand and has a strong optimization capability. However, the SMA is not suitable for …

Word clouds have emerged as a straightforward and visually appealing visualization
method for text. They are used in various contexts as a means to provide an overview by …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

Osteoporosis is a frequent bone disease without typical early symptoms but with serious
complications eg low-energy bone fractures. Patients with risk factors should be screened …

Misclassification costs of minority class data in real-world applications can be very high. This
is a challenging problem especially when the data is also high in dimensionality because of …

The traditional feature selection methods are not suitable for imbalanced data as they tend
to be biased towards the majority class. This problem is particularly acute in the field of …

The main goal of this study is to produce a landslide susceptibility map in the Wanzhou
section of the Three Gorges reservoir area (China) with a weighted gradient boosting …

High dimensionality, also known as the curse of dimensionality, is still a major challenge for
automatic classification solutions. Accordingly, several feature selection (FS) strategies have …