Modeling recurring concepts in data streams: a graph-based framework

Classifying a stream of non-stationary data with recurrent drift is a challenging task and has been considered as an interesting problem in recent years. All of the existing approaches handling recurrent concepts maintain a pool of concepts/classifiers and use that pool for future classifications to reduce the error on classifying the instances from a recurring concept. However, the number of classifiers in the pool usually grows very fast as the accurate detection of an underlying concept is a challenging task in itself. Thus, there may be many concepts in the pool representing the same underlying concept. This paper proposes the GraphPool framework that refines the pool of concepts by applying a merging mechanism whenever necessary: after receiving a new batch of data, we extract a concept representation from the current batch considering the correlation among features. Then, we compare the current batch representation to the concept representations in the pool using a statistical multivariate likelihood test. If more than one concept is similar to the current batch, all the corresponding concepts will be merged. GraphPool not only keeps the concepts but also maintains the transition among concepts via a first-order Markov chain. The current state is maintained at all times and new instances are predicted based on that. Keeping these transitions helps to quickly recover from drifts in some real-world problems with periodic behavior. Comprehensive experimental results of the framework on synthetic and real-world data show the effectiveness of the framework in terms of performance and pool management.