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.Comment: 33 Pages, Accepted for publication in The ACM Transactions on
Knowledge Discovery from Data (TKDD) in August 201
