Adaptive Generation Model: A New Ensemble Method

TL;DR

This paper introduces the Adaptive Generation Model (AGM), an ensemble method that enhances stacking by expanding model width and depth, incorporating feature augmentation, and demonstrating improved accuracy across multiple datasets.

Contribution

The paper presents a novel ensemble approach based on stacking and gcForest ideas, with adaptive horizontal and vertical expansion and feature augmentation.

Findings

AGM outperforms previous models on 7 datasets.

Incorporating feature augmentation improves accuracy.

Vertical and horizontal expansion enhances model robustness.

Abstract

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.