Evolving systems unfolds from the interaction and cooperation between systems with
adaptive structures, and recursive methods of machine learning. They construct models and …

Abstract Concept drift is a common and important issue in streaming data analysis and
mining. Thus far, many concept drift detection methods have been proposed but may not be …

Detection of anomalous behaviors in data centers is crucial to predictive maintenance and
data safety. With data centers, we mean any computer network that allows users to transmit …

Drift detection methods identify changes in data streams. Such changes are called concept
drifts. Existing drift detection methods often assume that the input is a noise-free data stream …

During the last 15 to 20 years, evolving (neuro-) fuzzy systems (E (N) FS) have enjoyed
more and more attraction in the context of data stream mining and modeling processes. This …

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

This paper proposes a hybrid architecture based on neural networks, fuzzy systems, and n-
uninorms for solving pattern classification problems, termed as ENFS-Uni0 (short for …

Big Data advancements motivate researchers to develop and improve intelligent models to
deal efficiently and effectively with data. In this scenario, time series forecasting obtains even …

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 …

This article presents a new design of experiment approach based on an evolving neuro-
fuzzy model. The input of the process is proposed by a space-filling method that uses a …