Class imbalance is an active research area in the machine learning community. However,
existing and recent literature showed that class overlap had a higher negative impact on the …

Class imbalanced datasets are common across different domains including health, security,
banking and others. A typical supervised learning algorithm tends to be biased towards the …

Classification of imbalanced datasets has attracted substantial research interest over the
past decades. Imbalanced datasets are common in several domains such as health, finance …

In this paper, we propose a new methodology for learning evolving fuzzy systems (EFS) from
data streams in terms of on-line regression/system identification problems. It comes with …

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 …

We propose a new clustering method, which is dynamic in the sense that it updates its
structure (cluster partition) permanently based on new incoming data samples. As it …

This chapter provides a round picture of the development and advances in the field of
evolving fuzzy systems (EFS) made during the last decade since their first appearance in …

With strong penetrability and high resolution, synthetic aperture radar (SAR) images have
been widely used in remote sensing image change detection. With the essence of heuristics …

Background and objective: Aimed at the shortcomings of using time interval (TA 2→ P 2)
between the sounds produced by the aortic valve closure (A 2) and the pulmonary valve …

Bayesian networks are a powerful tool for modelling multivariate random variables.
However, when applied in practice, for example, for industrial projects, problems arise …