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 …

New class detection and effective model expansion are of great importance in incremental
data mining. In open incremental data environments, data often come with novel classes, eg …

Learning with streaming data has attracted extensive research interest in recent years.
Existing online learning approaches have specific assumptions regarding data streams …

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 …

Multi-source streaming feature selection in an online manner has attracted considerable
attention, from researchers because it can reduce the dimensionality of heterogeneous big …

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 …

Constraint-based relevant feature selection using the Markov blanket (MB) discovery in
Bayesian network (BN) has attracted widespread attention in diverse data mining …

Learning with data streams has recently been the focus of extensive research and various
solutions have been proposed. However, most such studies assume that the features remain …

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 …