In this paper, we propose an elegant solution that is directly addressing the bottlenecks of
the traditional deep learning approaches and offers an explainable internal architecture that …

This book is a product of focused research over the last several years and brings in one
place the fundamentals of a new methodological approach to machine learning that is …

In this paper, an approach to autonomous learning of a multimodel system from streaming
data, named ALMMo, is proposed. The proposed approach is generic and can easily be …

Data stream mining presents notable challenges in the form of concept drift and evolution.
Existing learning algorithms, typically designed within a supervised learning framework …

In this paper, a new type of multilayer rule-based classifier is proposed and applied to image
classification problems. The proposed approach is entirely data-driven and fully automatic. It …

In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The
proposed approach identifies prototypes from the observed data through an offline training …

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 …

Automatic epilepsy detection is of great significance for the diagnosis and treatment of
patients. Most detection methods are based on patient-specific models and have achieved …

Evolving classifiers and especially evolving fuzzy classifiers have been established as a
prominent technique for addressing the recent demands in building classifiers in an …

Multi-label classification has attracted much attention in the machine learning community to
address the problem of assigning single samples to more than one (not necessarily non …