  In this article, we propose the Coopetititve Soft Gating Ensemble or CSGE for
general machine learning tasks and interwoven systems. The goal of machine
learning is to create models that generalize well for unknown datasets. Often,
however, the problems are too complex to be solved with a single model, so
several models are combined. Similar, Autonomic Computing requires the
integration of different systems. Here, especially, the local, temporal online
evaluation and the resulting (re-)weighting scheme of the CSGE makes the
approach highly applicable for self-improving system integrations. To achieve
the best potential performance the CSGE can be optimized according to arbitrary
loss functions making it accessible for a broader range of problems. We
introduce a novel training procedure including a hyper-parameter initialisation
at its heart. We show that the CSGE approach reaches state-of-the-art
performance for both classification and regression tasks. Further on, the CSGE
provides a human-readable quantification on the influence of all base
estimators employing the three weighting aspects. Moreover, we provide a
scikit-learn compatible implementation.
