In data stream environments, drift detection methods are used to identify when the context
has changed. This paper evaluates eight different concept drift detectors (ddm, eddm, pht …

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 …

We introduce an ensemble of classifiers-based approach for incremental learning of concept
drift, characterized by nonstationary environments (NSEs), where the underlying data …

Learning in nonstationary environments, also known as learning concept drift, is concerned
with learning from data whose statistical characteristics change over time. Concept drift is …

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 …

The evolving nature and accumulating volume of real-world data inevitably give rise to the
so-called “concept drift” issue, causing many deployed Case-Based Reasoning (CBR) …

The Belief Rule Base (BRB) is an expert system which can handle both qualitative and
quantitative information. One of the applications of the BRB is the Rule-base Inference …

We present an online ensemble approach, diversified dynamic weighted majority (DDWM)
to classify new data instances which have varying conceptual distributions. Our approach …

Most information sources in the current technological world are generating data sequentially
and rapidly, in the form of data streams. The evolving nature of processes may often cause …

Recently, stream data mining applications has drawn vital attention from several research
communities. Stream data is continuous form of data which is distinguished by its online …