论文标题
自适应生成模型：一种新的合奏方法
Adaptive Generation Model: A New Ensemble Method
论文作者
论文摘要
作为机器学习的一种常见方法，合奏方法用于从数据集中训练多个模型，并通过某些组合策略获得更好的结果。作为集合学习方法的代表，堆叠方法经常用于机器学习竞赛（例如Kaggle）。本文提出了基于GCFOREST的概念的堆叠模型的变体，即自适应生成模型（AGM）。这意味着自适应生成不仅是在水平方向上执行的，以扩大每个层模型的宽度，而且还在垂直方向上扩展模型的深度。对于AGM的基本模型，它们都来自预设的基本机器学习模型。此外，在层之间添加了一种功能增强方法，以进一步提高模型的整体准确性。最后，通过对7个数据集的比较实验，结果表明，年度股东周期股东的准确性优于其先前模型。
As a common method in Machine Learning, Ensemble Method is used to train multiple models from a data set and obtain better results through certain combination strategies. Stacking method, as representatives of Ensemble Learning methods, is often used in Machine Learning Competitions such as Kaggle. This paper proposes a variant of Stacking Model based on the idea of gcForest, namely Adaptive Generation Model (AGM). It means that the adaptive generation is performed not only in the horizontal direction to expand the width of each layer model, but also in the vertical direction to expand the depth of the model. For base models of AGM, they all come from preset basic Machine Learning Models. In addition, a feature augmentation method is added between layers to further improve the overall accuracy of the model. Finally, through comparative experiments on 7 data sets, the results show that the accuracy of AGM are better than its previous models.
