Data stream mining has become a novel research topic of growing interest in knowledge
discovery. Most proposed algorithms for data stream mining assume that each data block is …

Mining data streams of changing class distributions is important for real-time business
decision support. The stream classifier must evolve to reflect the current class distribution …

The majority of online learners assume that the data distribution to be learned is established
in advance. There are many real-world problems where the distribution of the data changes …

The major problem of online learning or incremental learning is that, target function is
frequently changing over time. This problem is commonly known as concept drift. Concept …

Data stream mining has gained growing attentions due to its wide emerging applications
such as target marketing, email filtering and network intrusion detection. In this paper, we …

Online learning involves extracting information from large quantities of data (streams)
usually affected by changes in the distribution (concept drift). A drift detector is a small …

In this thesis, we investigate the problem of supervised classification on a data stream
subject to concept drifts. In order to learn in this environment, we claim that a successful …

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 …

Due to the unprecedented speed and volume of generated raw data in most of applications,
data stream mining has attracted a lot of attention recently. Methods for solving these …

Mining feature evolvable streams has gained increasing attention in recent years. However,
most existing approaches are designed for stationary data streams (ie, data streams without …