Adaptive Generation Model: A New Ensemble Method

09/14/2020

∙

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.

READ FULL TEXT

Adaptive Generation Model: A New Ensemble Method

A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection

Stacking with Neural network for Cryptocurrency investment

Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions

Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market

A Comparative Analysis of the Ensemble Methods for Drug Design

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning

Adaptive Generation Model: A New Ensemble Method

Related Research

A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

AutoDES: AutoML Pipeline Generation of Classification with Dynamic Ensemble Strategy Selection

Stacking with Neural network for Cryptocurrency investment

Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions

Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market

A Comparative Analysis of the Ensemble Methods for Drug Design

Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning