Classification rule mining for a stream of perennial objects

Abstract

We study classification over a slow stream of complex objects like customers or students. The learning task must take into account that an object's label is influenced by incoming data from adjoint, fast streams of transactions, e.g. customer purchases or student exams, and that this label may even change over time. This task involves combining the streams, and exploiting associations between the target label and attribute values in the fast streams. We propose a method for the discovery of classification rules over such a confederation of streams, and we use it to enhance a decision tree classifier. We show that the new approach has competitive predictive power while building much smaller decision trees than the original classifier. © Springer-Verlag Berlin Heidelberg 2011.