The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

In recent years, the availability of time series streaming information has been growing
enormously. Learning from real-time data has been receiving increasingly more attention …

Concept drift describes unforeseeable changes in the underlying distribution of streaming
data overtime. Concept drift research involves the development of methodologies and …

Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Online streaming feature selection (OSFS) has attracted extensive attention during the past
decades. Current approaches commonly assume that the feature space of fixed data …

Data‐driven epoch, the development of machine learning (ML) in materials and device
design is an irreversible trend. Its ability and efficiency to handle nonlinear and game …

The ongoing outbreak of the COVID-19 pandemic prevails as an ultimatum to the global
economic growth and henceforth, all of society since neither a curing drug nor a preventing …

Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …

Applications that generate huge amounts of data in the form of fast streams are becoming
increasingly prevalent, being therefore necessary to learn in an online manner. These …