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 …

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 many real-world applications, data are collected in the form of a stream, whose feature
space can evolve over time. For instance, in the environmental monitoring task, features can …

Multi-label streaming feature selection has attracted extensive attention in diverse big data
applications. However, most existing works focused on the scenarios where labels are …

Learning with streaming data has received extensive attention during the past few years.
Existing approaches assume that the feature space is fixed or changes by following explicit …

This paper explores a new online learning problem where the input sequence lives in an
over-time varying feature space and the ground-truth label of any input point is given only …

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 …

This paper investigates a new online learning problem with doubly-streaming data, where
the data streams are described by feature spaces that constantly evolve, with new features …

This paper explores a new online learning problem where the data streams are generated
from an over-time varying feature space, in which the random variables are of mixed data …