The digital universe is growing rapidly. The volume of data generated per annum is in the
order of zeta bytes due to the proliferation of the Internet. Many real-world applications …

The concept changes in continuously evolving data streams are termed as concept drifts. It
is required to address the problems caused due to concept drift and adapt according to the …

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 …

World is generating immeasurable amount of data every minute, that needs to be analyzed
for better decision making. In order to fulfil this demand of faster analytics, businesses are …

The continuously evolving data stream are termed as concept drifts. It is obviously required
to address the problems caused due to concept drift and adapt according to the concept …

In a database, the concept of an example might change along with time, which is known as
concept drift. When the concept drift occurs, the classification model built by using the old …

Concept drift refers to changes in the underlying data distribution of data streams over time.
A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it …

The term “concept drift” refers to a change in statistical distribution of the data. In machine
learning and predictive analysis, a fundamental assumption exits which reasons that the …

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

The objective of this work is to develop a predictive model when data batches are collected
in a sequential manner. With streaming data, information is constantly being updated and a …