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.

本文介绍了一种新的数据流集成分类器，Geometrically Optimum and Online-Weighted Ensemble (GOOWE)。GOOWE通过使用包含最近数据实例的滑动窗口，为组成分类器分配最优权重。作者在真实世界数据集和MOA库的合成数据生成器中使用GOOWE，通过两个情景分析了该权重系统对预测准确性的影响，并将其与8种其他集成分类器进行了比较。实验结果表明，与基线相比，GOOWE在不同类型的概念漂移下提供了更好的反应，并且具有保守的时间和内存要求。

GOOWE: 面向动态数据流的几何最佳化和在线加权集成分类器