With outstanding features, machine learning (ML) has become the backbone of numerous
applications in wireless networks. However, the conventional ML approaches face many …

With outstanding features, Machine Learning (ML) has been the backbone of numerous
applications in wireless networks. However, the conventional ML approaches have been …

This special issue explores big data analytics and applications for logistics and supply chain
management by examining novel methods, practices, and opportunities. The articles present …

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 …

Learning from imbalanced data streams is one of the challenges associated with
classification algorithms and learning classifiers. The goal of this paper is to propose and …

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

In this paper, a novel evolving fuzzy-rule-based classifier, termed parsimonious classifier
(pClass), is proposed. pClass can drive its learning engine from scratch with an empty rule …

Zero-shot learning (ZSL) is a successful paradigm for categorizing objects from the
previously unseen classes. However, it suffers from severe performance degradation in the …

This chapter provides an all-round picture of the development and advances in the fields of
evolving fuzzy systems (EFS) and evolving neuro-fuzzy systems (ENFS) which have been …

Abstract Motivated by the Statistical Learning Theory (SLT), which provides a theoretical
framework to ensure when supervised learning algorithms generalize input data, this …