Random Projection in the Presence of Concept Drift in Supervised Environments

Abstract

In static environments Random Projection (RP) is a popular and efficient technique to preprocess high-dimensional data and to reduce its dimensionality. While RP has been widely used and evaluated in stationary data analysis scenarios, non-stationary environments are not well analyzed. In this paper we provide an evaluation of RP on streaming data including a concept of altering dimensions. We discuss why RP can be used in this scenario and how it can handle stream specific situations like concept drift. We also provide experiments with RP on streaming data, using state-of-the-art streaming classifiers like Adaptive Hoeffding Tree and concept drift detectors on streams containing altering dimensions.