In an era of growing data complexity and volume and the advent of big data, feature
selection has a key role to play in helping reduce high-dimensionality in machine learning …

Concept drift refers to a non stationary learning problem over time. The training and the
application data often mismatch in real life problems. In this report we present a context of …

Abstract Concept drift is a phenomenon that commonly happened in data streams and need
to be detected, because it means the statistical properties of a target variable, which the …

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 …

An aptamer is a single-stranded DNA or RNA that binds to a specific target with high binding
affinity. Aptamers are developed through the process of systematic evolution of ligands by …

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 mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …

Data streams containing objects that are (or can be) associated with more than one label at
the same time are ubiquitous. Typical types of data associated with more than one labels are …

When using statistical models (such as a classifier) in a streaming environment, there is
often a need to detect and adapt to concept drifts to mitigate any deterioration in the model's …

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 …