Detecting concept drifts and reducing the impact from the noise in real applications of data
streams are challenging but valuable for inductive learning. It is especially a challenge in a …

Classification with concept-drifting data streams has found wide applications. However,
many classification algorithms on streaming data have been designed for fixed features of …

Although a vast majority of inductive learning algorithms has been developed for handling of
the concept drifting data streams, especially the ones in virtue of ensemble classification …

It is challenging to use traditional data mining techniques to deal with real-time data stream
classifications. Existing mining classifiers need to be updated frequently to adapt to the …

Data stream mining has become an important research area over the past decade due to the
increasing amount of data available today. Sources from various domains generate a near …

Recent years have witnessed a large body of research work on mining concept drifting data
streams, where a primary assumption is that the up-to-date data chunk and the yet-to-come …

Abstract Concept drift, change in the underlying distribution that data points come from, is an
inevitable phenomenon in data streams. Due to increase in the number of data streams' …

Predictive models operating on the evolving data streams are dynamic. The performance of
a model will deteriorate eventually when it suffers the effect of concept drift. The learning …

Mining concept drifting data streams is a defining challenge for data mining research.
Recent years have seen a large body of work on detecting changes and building prediction …

Recently, mining data streams with concept drifts for actionable insights has become an
important and challenging task for a wide range of applications including credit card fraud …