论文标题
自动：使用动态集成策略选择的Automl管道生成分类
AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection
论文作者
论文摘要
近年来，自动化的机器学习已经取得了非凡的技术发展，并且建立自动化的机器学习管道现在是一项重要任务。模型集合是结合多个模型以获得更好，更健壮的模型的技术。但是，现有的自动化机器学习在处理模型集合（固定集成策略）（例如堆叠的概括）时往往很简单。在不同的集合方法上，尤其是集合选择，固定的集成策略限制了模型性能的上限。在本文中，我们为自动化机器学习提供了一个新颖的框架。我们的框架结合了动态合奏选择的进步，据我们的最大知识，我们的方法是Automl领域的第一个搜索和优化合奏策略的方法。在比较实验中，我们的方法在42个分类数据集中从OpenML平台使用相同的CPU时间优于最先进的机器学习框架。关于我们框架的消融实验验证了我们提出的方法的有效性。
Automating machine learning has achieved remarkable technological developments in recent years, and building an automated machine learning pipeline is now an essential task. The model ensemble is the technique of combining multiple models to get a better and more robust model. However, existing automated machine learning tends to be simplistic in handling the model ensemble, where the ensemble strategy is fixed, such as stacked generalization. There have been many techniques on different ensemble methods, especially ensemble selection, and the fixed ensemble strategy limits the upper limit of the model's performance. In this article, we present a novel framework for automated machine learning. Our framework incorporates advances in dynamic ensemble selection, and to our best knowledge, our approach is the first in the field of AutoML to search and optimize ensemble strategies. In the comparison experiments, our method outperforms the state-of-the-art automated machine learning frameworks with the same CPU time in 42 classification datasets from the OpenML platform. Ablation experiments on our framework validate the effectiveness of our proposed method.
