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 …

The prevalence of mobile phones, the internet-of-things technology, and networks of
sensors has led to an enormous and ever increasing amount of data that are now more …

While both cost-sensitive learning and online learning have been studied separately, these
two issues have seldom been addressed simultaneously. Yet, there are many applications …

Real-world data streams pose a unique challenge to the implementation of machine
learning (ML) models and data analysis. A notable problem that has been introduced by the …

Abstract Concept drift poses significant challenges in the fields of machine learning and data
mining. At present, many existing algorithms struggle to maintain low error rates or require …

Among the many issues related to data stream applications, those involved in predictive
tasks such as classification and regression, play a significant role in Machine Learning (ML) …

Changes in the data distribution (concept drift) makes online learning a challenge that is
progressively attracting more attention. This paper proposes Boosting-like Online Learning …

Abstract “Concept drift” and class imbalance are two challenges for supervised
classifiers.“Concept drift”(or non-stationarity) is changes in the underlying function being …

Abstract Concept drift (non-stationarity) and class imbalance are two important challenges
for supervised classifiers.“Concept drift”(or non-stationarity) refers to changes in the …

Nowadays, data stream mining is a very hot and high attention research field due to the real-
time industrial applications from different sources are generating amount of data …