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 …

Ensemble-based methods are among the most widely used techniques for data stream
classification. Their popularity is attributable to their good performance in comparison to …

Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

Feature selection, which plays an important role in high-dimensional data analysis, is
drawing increasing attention recently. Finding the most relevant and important features for …

Abstract The Internet of Things (IoT) offers a lot of benefits for building smart cities. Such
cities will be able to utilize a huge number of heterogeneous IoT devices that can generate a …

Data stream classification poses many challenges to the data mining community. In this
paper, we address four such major challenges, namely, infinite length, concept-drift, concept …

Unlabelled data appear in many domains and are particularly relevant to streaming
applications, where even though data is abundant, labelled data is rare. To address the …

Abstract Motivated by the Statistical Learning Theory (SLT), which provides a theoretical
framework to ensure when supervised learning algorithms generalize input data, this …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

This paper explores a new online learning problem where the input sequence lives in an
over-time varying feature space and the ground-truth label of any input point is given only …