Major assumptions in computational intelligence and machine learning consist of the
availability of a historical dataset for model development, and that the resulting model will, to …

The incompleteness of data samples in data streams always affects the performance of
learning model. In order to learn data streams with missing values from features and a large …

Increasing demands on effluent quality and loads call for an improved control, monitoring,
and fault detection of waste-water treatment plants (WWTPs). Improved control and …

In recent years, many nearest neighbor algorithms based on fuzzy sets theory have been
developed. These methods form a field, known as fuzzy nearest neighbor classification …

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 …

In this paper, we present a new methodology for conducting active learning in a single-pass
on-line learning context. Single-pass active learning can be understood as an approach for …

In this paper, we address the problem of integrating new classes on the fly into on-line
classification systems. The main focus is on visual inspection tasks, although the concepts …

In this paper we present a self-tuning of two degrees-of-freedom control algorithm that is
designed for use on a non-linear single-input single-output system. The control algorithm is …

Evolving participatory learning (ePL) modeling joins the concepts of participatory learning
and evolving fuzzy systems. It uses data streams to continuously adapt the structure and …

This paper presents the solving of the petrol sales volume estimation problem given by the
Task Force on Competitions, Fuzzy Systems Technical Committee IEEE Computational …