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 …

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 …

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 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 …

In a typical data stream classification task, it is assumed that the total number of classes are
fixed. This assumption may not be valid in a real streaming environment, where new classes …

It is challenging to use traditional data mining techniques to deal with real-time data stream
classifications. Existing mining classifiers need to be updated frequently to adapt to 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 …

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 consider the problem of resource allocation in mining multiple data streams. Due to the
large volume and the high speed of streaming data, mining algorithms must cope with the …