Most of studies on streaming data classification are based on the assumption that data can
be fully labeled. However, in real-life applications, it is impractical and time-consuming to …

Recent years have witnessed growing interests in online incremental learning. However,
there are three major challenges in this area. The first major difficulty is concept drift, that is …

Data streams are viewed as a sequence of relational tuples (eg, sensor readings, call
records, web page visits) that continuously arrive at time-varying and possibly unbound …

Neural networks have enjoyed tremendous success in many areas over the last decade.
They are also receiving more and more attention in learning from data streams, which is …

Data quality is deemed as determinant in the knowledge extraction process. Low-quality
data normally imply low-quality models and decisions. Discretization, as part of data …

Learning with streaming data has attracted extensive research interest in recent years.
Existing online learning approaches have specific assumptions regarding data streams …

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 …

As data streams are gaining prominence in a growing number of emerging application
domains, classification on data streams is becoming an active research area. Currently, the …

In this era of big data, stream data classification which is one of typical data stream
applications has become more and more significant and challengeable. In these …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …