Tracking recurrent concept drift in streaming data using ensemble classifiers

Abstract

Streaming data may consist of multiple drifting concepts each having its own underlying data distribution. We present an ensemble learning based approach to handle the data streams having multiple underlying modes. We build a global set of classifiers from sequential data chunks; ensembles are then selected from this global set of classifiers, and new classifiers created if needed, to represent the current concept in the stream. The system is capable of performing any-time classification and to detect concept drift in the stream. In streaming data historic concepts are likely to reappear so we don't delete any of the historic classifiers. Instead, we judiciously select only pertinent classifiers from the global set while forming the ensemble set for a classification task.