Online data stream learning requires to process each training example upon arrival.
Machine learning algorithms designed to process data in this manner are particularly …

Abstract Concept drifts and class imbalance are two major challenges in supervised data
stream classification, whereas their co-occurrence complicates the learning problem further …

In real-world applications, the process generating the data might suffer from nonstationary
effects (eg, due to seasonality, faults affecting sensors or actuators, and changes in the …

Although the issues of concept drift and class imbalance have been studied separately, the
joint problem is underexplored even though it has received increasing attention. Concept …

Abstract Concept drifts and class imbalance are two primary challenges in supervised data
stream classification, whereas their co-occurrence presents a more complicated learning …

Learning nonstationary data streams has been well studied in recent years. However, most
of the researches assume that the class imbalance of data streams is relatively balanced …

Class imbalance introduces additional challenges when learning classifiers from concept
drifting data streams. Most existing work focuses on designing new algorithms for dealing …

Data are everywhere. From new emerging topics in social networks to pressure and
vibration levels of industrial machinery, from traffic congestion to drilling in an oil ring, data …

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

In IoT environment applications generate continuous non-stationary data streams with in-
built problems of concept drift and class imbalance which cause classifier performance …