Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm

TLDR

Data stream mining attracts attention across sensor networks, banking, and telecommunications, yet reacting to concept drift—unforeseen changes in the underlying distribution—remains a key challenge, and most existing algorithms specialize in only one drift type. The authors introduce the Accuracy Updated Ensemble (AUE2), a classifier designed to respond effectively to a variety of drift scenarios. AUE2 merges accuracy‑based weighting from block‑based ensembles with the incremental learning of Hoeffding Trees, and is benchmarked against 11 state‑of‑the‑art stream methods across diverse drift conditions. Experimental results show that AUE2 delivers the highest average classification accuracy while using less memory, making it suitable for environments with multiple drift types and for static settings.

Abstract

Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges in learning from data streams is reacting to concept drift, i.e., unforeseen changes of the stream's underlying data distribution. Several classification algorithms that cope with concept drift have been put forward, however, most of them specialize in one type of change. In this paper, we propose a new data stream classifier, called the Accuracy Updated Ensemble (AUE2), which aims at reacting equally well to different types of drift. AUE2 combines accuracy-based weighting mechanisms known from block-based ensembles with the incremental nature of Hoeffding Trees. The proposed algorithm is experimentally compared with 11 state-of-the-art stream methods, including single classifiers, block-based and online ensembles, and hybrid approaches in different drift scenarios. Out of all the compared algorithms, AUE2 provided best average classification accuracy while proving to be less memory consuming than other ensemble approaches. Experimental results show that AUE2 can be considered suitable for scenarios, involving many types of drift as well as static environments.