An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise

Abstract

Recent years have witnessed a large body of research work on mining concept drifting data streams, where a primary assumption is that the up-to-date data chunk and the yet-to-come data chunk share identical distributions, so classifiers with good performance on the up-to-date chunk would also have a good prediction accuracy on the yet-to-come data chunk. This “stationary assumption”, however, does not capture the concept drifting reality in data streams. More recently, a “learnable assumption” has been proposed and allows the distribution of each data chunk to evolve randomly. Although this assumption is capable of describing the concept drifting in data streams, it is still inadequate to represent real-world data streams which usually suffer from noisy data as well as the drifting concepts. In this paper, we propose a Realistic Assumption which asserts that the difficulties of mining data streams are mainly caused by both concept drifting and noisy data chunks. Consequently, we present a new Aggregate Ensemble (AE) framework, which trains base classifiers using different learning algorithms on different data chunks. All the base classifiers are then combined to form a classifier ensemble through model averaging. Experimental results on synthetic and real-world data show that AE is superior to other ensemble methods under our new realistic assumption for noisy data streams.