In this paper, we propose three criteria for efficient sample selection in case of data stream
regression problems within an online active learning context. The selection becomes …

The central purpose of this survey is to provide readers an insight into the recent advances
and challenges in on-line active learning. Active learning has attracted the data mining and …

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 …

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 …

Online active learning is a paradigm in machine learning that aims to select the most
informative data points to label from a data stream. The problem of minimizing the cost …

Where active learning with uncertainty sampling is used to generate training sets for
classification applications, it is sensible to use the same type of classifier to select the most …

Anomaly detection in todays industrial environments is an ambitious challenge to detect
possible faults/problems which may turn into severe waste during production, defects, or …

Evolving fuzzy neural classifiers are incremental, adaptive models that use new samples to
update the architecture and parameters of the models with new incoming data samples …

The physico-chemical properties of Melamine Formaldehyde (MF) based thermosets are
largely influenced by the degree of polymerization (DP) in the underlying resin. On-line …

We propose an approach for the automated prediction of possible quality deteriorations at
injection molding machines using data-driven models. This approach relies on data solely …