In online learning, the concept drifts refers to the situations where the objective variable
conforming to the input data changes over time. This change in distribution of data over time …

Various types of online learning algorithms have been developed so far to handle concept
drift in data streams. We perform more detailed evaluation of these algorithms through new …

This paper presents recurring concept drifts (RCD), a framework that offers an alternative
approach to handle data streams that suffer from recurring concept drifts (on-line learning). It …

The digital universe is growing rapidly. The volume of data generated per annum is in the
order of zeta bytes due to the proliferation of the Internet. Many real-world applications …

Abstract Concept drift represents that the underlying data generating distribution changes
over time and it is a common phenomenon in a stream of data sets. In particular, concept …

The major problem of online learning or incremental learning is that, target function is
frequently changing over time. This problem is commonly known as concept drift. Concept …

The concept changes in continuously evolving data streams are termed as concept drifts. It
is required to address the problems caused due to concept drift and adapt according to the …

Recent advances in computational intelligent systems have focused on addressing complex
problems related to the dynamicity of the environments. In increasing number of real world …

Learning unlabeled data in a drifting environment still receives little attention. This paper
presents a concept tracker algorithm for learning concept drift that exploits unlabeled data. In …