A benchmark of classifiers on feature drifting data streams

Abstract

The ever increasing data generation confronts both practitioners and researchers on handling massive and sequentially generated amounts of information, the so-called data streams. In this context, a lot of effort has been put on the extraction of useful patterns from streaming scenarios. Learning from data streams embeds a variety of problems, and by far, the most challenging is concept drift, i.e. changes in data distribution. In this paper, we focus on a specific type of drift uncommonly assessed in the literature: feature drifts. Feature drifts occur whenever a subset of features becomes, or ceases to be, relevant to the concept to be learned. We propose and review several feature drifting data stream generators and use them to benchmark state-of-the-art data stream classification algorithms and their combination with drift detectors. Results show that, although drift detectors enable slight quicker recovery to feature drifts, best results are obtained by Hoeffding Adaptive Tree, the only learner that performs dynamic feature selection as streams progress.