To decide if an update to a data stream classifier is necessary, existing sliding window
based techniques monitor classifier performance on recent instances. If there is a significant …

Mining non-stationary stream is a challenging task due to its unique property of infinite
length and dynamic characteristics let alone the issues of concept drift, concept evolution …

The nature of data streams requires classification algorithms to be real-time, efficient, and
able to cope with high-dimensional data that are continuously arriving. It is a known fact that …

Data stream mining has been receiving increased attention due to its presence in a wide
range of applications, such as sensor networks, banking, and telecommunication. One of 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 …

Most approaches to classifying data streams either divide the stream into fixed-size chunks
or use gradual forgetting. Due to evolving nature of data streams, finding a proper size or …

Abstract Concept drift constitutes a challenging problem for the machine learning and data
mining community that frequently appears in real world stream classification problems. It is …

Abstract Concept drift may lead to a sharp downturn in the performance of streaming in data-
based algorithms, caused by unforeseeable changes in the underlying distribution of data …

In the era of data mining, the research industry has great attention to data stream mining as
well as it has a great impact on a wide range of applications like networking …

Data stream classification plays an important role in modern data analysis, where data
arrives in a stream and needs to be mined in real time. In the data stream setting the …