Mining evolving data streams for concept drifts has gained importance in applications like
customer behavior analysis, network intrusion detection, credit card fraud detection. Several …

Classification is an important data analysis tool that uses a model built from historical data to
predict class labels for new observations. More and more applications are featuring data …

Data stream mining has become an interesting analysis topic and it is a growing interest in
data discovery method. There are several applications supporting stream data processing …

Most existing concept-drifting data streams classification approaches assume that the true
label of the instance in the data streams can be accessed right after it is classified and utilize …

Data streams mining has become a novel research topic of growing interest in knowledge
discovery. Because of the high speed and huge size of data set in data streams, the …

Large numbers of data streams are today generated in many fields. A key challenge when
learning from such streams is the problem of concept drift. Many methods, including many …

Data stream classification poses many challenges, most of which are not addressed by the
state-of-the-art. We present DXMiner, which addresses four major challenges to data stream …

The rapid growth of the information technology accelerates organizations to generate vast
volumes of high-velocity data streams. The concept drift is a crucial issue, and discovering …

This paper presents a novel ensemble learning method based on evolutionary algorithms to
cope with different types of concept drifts in non-stationary data stream classification tasks. In …

With the era of IOT, every device is bound to generate data and every digital footprint is
noted. This advances in the technology gave rise to data generation at large stature and …