Novelty detection is the task of classifying test data that differ in some respect from the data
that are available during training. This may be seen as “one-class classification”, in which a …

Applying Fourier-transform infrared (FTIR) spectroscopy (or related technologies such as
Raman spectroscopy) to biological questions (defined as biospectroscopy) is relatively …

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 …

Autonomous Learning Systems is the result of over a decade of focused research and
studies in this emerging area which spans a number of well-known and well-established …

A new approach to the online classification of streaming data is introduced in this paper. It is
based on a self-developing (e volving) fuzzy-rule-based (FRB) classifier system of T akagi-S …

It is a well‐known fact that nowadays we are faced not only with large data sets that we need
to process quickly, but with huge data streams. Special requirements are also placed by the …

In this paper we present two novel approaches for on-line evolving fuzzy classifiers, called
eClass and FLEXFIS-Class. Both methods can be applied with different model architectures …

Information granules are complex entities that arise in the process of abstraction of data and
derivation of knowledge. The automatic generation of information granules from data is an …

In this paper, we propose a two-stage algorithm for real-time fault detection and identification
of industrial plants. Our proposal is based on the analysis of selected features using …

In this position paper, we are discussing achievements and open issues in the
interpretability of evolving fuzzy systems (EFS). In addition to pure on-line complexity …