GOOWE: Geometrically Optimum and Online-Weighted Ensemble Classifier for Evolving Data Streams

TL;DR

GOOWE is a novel online ensemble classifier that assigns optimal weights to component classifiers using a geometric approach based on Euclidean distances, improving adaptability and accuracy in evolving data streams.

Contribution

This paper introduces GOOWE, the first online ensemble method to adaptively assign weights via a geometric least squares approach, enhancing performance on evolving data streams.

Findings

GOOWE outperforms 8 state-of-the-art ensemble classifiers in accuracy.

It reacts more effectively to different types of concept drift.

The method requires conservative time and memory resources.

Abstract

Designing adaptive classifiers for an evolving data stream is a challenging task due to the data size and its dynamically changing nature. Combining individual classifiers in an online setting, the ensemble approach, is a well-known solution. It is possible that a subset of classifiers in the ensemble outperforms others in a time-varying fashion. However, optimum weight assignment for component classifiers is a problem which is not yet fully addressed in online evolving environments. We propose a novel data stream ensemble classifier, called Geometrically Optimum and Online-Weighted Ensemble (GOOWE), which assigns optimum weights to the component classifiers using a sliding window containing the most recent data instances. We map vote scores of individual classifiers and true class labels into a spatial environment. Based on the Euclidean distance between vote scores and ideal-points, and using the linear least squares (LSQ) solution, we present a novel, dynamic, and online weighting approach. While LSQ is used for batch mode ensemble classifiers, it is the first time that we adapt and use it for online environments by providing a spatial modeling of online ensembles. In order to show the robustness of the proposed algorithm, we use real-world datasets and synthetic data generators using the MOA libraries. First, we analyze the impact of our weighting system on prediction accuracy through two scenarios. Second, we compare GOOWE with 8 state-of-the-art ensemble classifiers in a comprehensive experimental environment. Our experiments show that GOOWE provides improved reactions to different types of concept drift compared to our baselines. The statistical tests indicate a significant improvement in accuracy, with conservative time and memory requirements.