The age of online data stream and dynamic environments results in the increasing demand
of advanced machine learning techniques to deal with concept drifts in large data streams …

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network
configuration having lower generalization power than those of deep structures. This article …

Evolving fuzzy classifiers (EFCs) have achieved immense success in dealing with
nonstationary data streams because of their flexible characteristics. Nonetheless, most real …

Current models and algorithms have been increasingly required to learn in a nonstationary
environment because the phenomenon of concept drift (or pattern shift) may occur, that is …

Recognizing the weakness of the existing evolving fuzzy systems (EFSs) where the
selection and determination of thresholds for the structure and parameter learning are …

In this paper, we present new approaches to handling drift and shift in on-line data streams
with the help of evolving fuzzy systems (EFS), which are characterized by the fact that their …

The proposal of a meta-cognitive learning machine that embodies the three pillars of human
learning: what-to-learn, how-to-learn, and when-to-learn, has enriched the landscape of …

This paper proposes an online learning algorithm for data streams, namely self-evolving
fuzzy system (SEFS). Unlike the fixed control parameters commonly used in evolving fuzzy …

As a type of evolving-fuzzy system, the evolving-fuzzy-neuro (EFN) system uses the structure
inspired by neural networks to determine its parameters (fuzzy sets and fuzzy rules), so EFN …

The aim of machine learning is to find hidden insights into historical data, and then apply
them to forecast the future data or trends. Machine learning algorithms optimize learning …