Time series prediction has great practical value in a wide range of real-world scenarios such
as stock market and retail. Existing methods typically face model aging issue caused by the …

As a popular strategy to tackle concept drift, chunk-based ensemble method adapts a new
concept by adjusting the weights of historical classifiers. However, most previous …

Many methods have been proposed to detect concept drift, ie, the change in the distribution
of streaming data, due to concept drift causes a decrease in the prediction accuracy of …

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 …

Several data stream applications involve recurring concepts, ie, concept drifts that change
the underlying distribution of the data to a distribution previously seen in the data stream …

Mining data streams for predictive analysis is one of the most interesting topics in machine
learning. With the drifting data distributions, it becomes important to build adaptive systems …

Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

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

Financial markets and weather prediction are generating streaming data at a rapid rate. The
frequent concept drifts in these data streams pose significant challenges to learners during …

The problem of concept drift has gained a lot of attention in recent years. This aspect is key
in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks …