Real-world problems are commonly characterized by a high feature dimensionality, which
hinders the modelling and descriptive analysis of the data. However, some of these data …

Abstract Knowledge discovery for data streaming requires online feature selection to reduce
the complexity of real-world datasets and significantly improve the learning process. This is …

Feature selection approaches aim to select a set of prominent features that best describe the
data to improve the efficiency without degrading the performance of the model. In many real …

Streaming feature selection (SFS), is the task of selecting the most informative features in
dealing with high-dimensional or incrementally growing problems. Several SFS algorithms …

In real-world applications, features can be generated continuously one by one or by groups,
such as image analysis and physical examination. Online streaming feature selection deals …

The feature selection method is the process of selecting only relevant features by removing
irrelevant or redundant features amongst the large number of features that are used to …

Online group streaming feature selection, as an essential online processing method, can
deal with dynamic feature selection tasks by considering the original group structure …

Many online platforms now generate data in a streaming manner, resulting in the continuous
production of new features. Multi-label data generation has also surged in recent years …

Streaming feature selection (SFS) is emerging as a key research direction that addresses
the nonstationary property of feature streams when the sample size is fixed. Most existing …

Bat algorithm (BA) is a new and promising metaheuristic search algorithm which could
outperform existing algorithms. However, BA can be easily trapped in a local optimum …