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 …

Recent years have witnessed the proliferation of techniques for streaming data mining to
meet the demands of many real-time systems, where high-dimensional streaming data are …

Feature Selection (FS) plays an important role in learning and classification tasks. Its
objective is to select the relevant and non-redundant features. Considering the huge number …

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 …

The idea of combining the active query strategy and the passive-aggressive (PA) update
strategy in online learning can be credited to the PA active (PAA) algorithm, which has …

In this paper, we propose a novel robust sparse online learning framework named
Adversarial Sparse Online Learning (ASOL) for high dimensional data streams, which is …

Feature selection can be a crucial factor in obtaining robust and accurate predictions. Online
feature selection models, however, operate under considerable restrictions; they need to …

Selection of features as a pre-processing stage is an essential issue in many machine
learning tasks (such as classification) to reduce data dimensionality as there are many …

Logistic regression, the Support Vector Machine (SVM), and least squares are well-studied
methods in the statistical and computer science community, with various practical …