Human intelligence comes from the capability to describe and make sense of the world
surrounding us, often in a lifelong manner. Online Learning (OL) allows a model to simulate …

Online learning with feature spaces that are not fixed but can vary over time renders a
seemingly flexible learning paradigm thus has drawn much attention. Unfortunately, two …

In this paper, we propose a new online learning algorithm tailored for data streams
described by varying feature spaces (VFS), wherein new features constantly emerge and old …

Feature selection, an essential technique in data mining, is often confined to batch learning
or online idealization of data scenarios despite its significance. Existing online feature …

Stream learning in dynamic feature space has evolved into an immensely popular field. This
problem assumes that each instance of the data stream may have different features, and the …

Sparse online learning has received extensive attention during the past few years. Most of
existing algorithms that utilize ℓ1-norm regularization or ℓ1-ball projection assume that the …

Recently, many researchers have focused on a novel type of data stream known as a feature
evolvable stream, wherein the existing features may become obsolete while new features …

In the real world, data is often vast and subject to rapid change. Consequently, an increasing
number of researchers have turned their attention to data streaming learning to mine the …

Wearable activity recognition plays an important role in human health monitoring. Traditional
wearable activity recognition models are trained in an offline mode with static and pre …

Most conventional literature on online learning implicitly assumes a static feature space.
However, in real-world applications, the feature space may vary over time due to the …