Data stream classification poses many challenges to the data mining community. In this
paper, we address four such major challenges, namely, infinite length, concept-drift, concept …

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 …

Developing effective and efficient data stream classifiers is challenging for the machine
learning community because of the dynamic nature of data streams. As a result, many data …

We present ActMiner, which addresses four major challenges to data stream classification,
namely, infinite length, concept-drift, concept-evolution, and limited labeled data. Most of the …

Classification plays a crucial role in mining the evolving data streams. The concept drift and
concept evolution are the major issues of data streams classification, which greatly affect the …

Streaming data is one of the attention receiving sources for concept-evolution studies. When
a new class occurs in the data stream it can be considered as a new concept and so the …

Data stream classification is one of the most challenging areas in the machine learning. In
this paper, we focus on three major challenges namely infinite length, concept-drift and …

Learning non-stationary data streams is challenging due to the unique characteristics of
infinite length and evolving property. Current existing works often concentrate on the …

Most existing data stream classification techniques ignore one important aspect of stream
data: arrival of a novel class. We address this issue and propose a data stream classification …

Concept-evolution has recently received a lot of attention in the context of mining data
streams. Concept-evolution occurs when a new class evolves in the stream. Although many …