论文标题
扩展的竞争柔软的门控合奏
Extended Coopetitive Soft Gating Ensemble
论文作者
论文摘要
本文是关于最近一种称为Cootititive Soft Soft Gating Ensemble（CSGE）的集合方法的延伸，及其在权力预测以及对骑自行车者的运动原始预测上的应用。 CSGE已成功地用于风力预测领域，超过该域中的常见算法。 CSGE的主要思想是将模型在不同方面的训练过程中对其观察到的性能进行加权。本文中的原始CSGE提出了几个扩展，使整体更加灵活和强大。扩展的CSGE（我们称为XCSGE）用于预测风能和太阳能农场的发电。此外，XCSGE适用于在驾驶员援助系统的背景下预测骑自行车者的运动状态。这两个领域都有不同的要求，是非平凡的问题，用于评估新颖XCSGE的各个方面。这两个问题在数据集的大小和功能数量上从根本上有所不同。电源预测是基于天气预报，这些预测会在其功能中发生波动。在骑自行车者的运动原始预测中，时间延迟有助于预测的困难。与最差的性能模型相比，XCSGE的预测性能提高了高达11％的预测性能和太阳能预测的30％。为了将骑自行车的人的运动原语分类，XCSGE的提高高达28％。评估包括与其他最先进的合奏方法的比较。我们可以使用Nemenyi事后测试来验证XCSGE结果明显更好。
This article is about an extension of a recent ensemble method called Coopetitive Soft Gating Ensemble (CSGE) and its application on power forecasting as well as motion primitive forecasting of cyclists. The CSGE has been used successfully in the field of wind power forecasting, outperforming common algorithms in this domain. The principal idea of the CSGE is to weight the models regarding their observed performance during training on different aspects. Several extensions are proposed to the original CSGE within this article, making the ensemble even more flexible and powerful. The extended CSGE (XCSGE as we term it), is used to predict the power generation on both wind- and solar farms. Moreover, the XCSGE is applied to forecast the movement state of cyclists in the context of driver assistance systems. Both domains have different requirements, are non-trivial problems, and are used to evaluate various facets of the novel XCSGE. The two problems differ fundamentally in the size of the data sets and the number of features. Power forecasting is based on weather forecasts that are subject to fluctuations in their features. In the movement primitive forecasting of cyclists, time delays contribute to the difficulty of the prediction. The XCSGE reaches an improvement of the prediction performance of up to 11% for wind power forecasting and 30% for solar power forecasting compared to the worst performing model. For the classification of movement primitives of cyclists, the XCSGE reaches an improvement of up to 28%. The evaluation includes a comparison with other state-of-the-art ensemble methods. We can verify that the XCSGE results are significantly better using the Nemenyi post-hoc test.
