Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems

TL;DR

The paper introduces Dense Adaptive Cascade Forest (daForest), a self-adaptive deep ensemble model that improves classification accuracy, resists performance degradation, and optimizes hyper-parameters efficiently, outperforming neural networks in some cases.

Contribution

It proposes a novel deep forest ensemble model with boosting, feed-forward layer connections, and hyper-parameter optimization, enhancing performance and efficiency over existing methods.

Findings

daForest outperforms original Cascade Forest in accuracy.

The model surpasses neural networks in some classification tasks.

It demonstrates significant performance improvements on benchmark datasets.

Abstract

Recent researches have shown that deep forest ensemble achieves a considerable increase in classification accuracy compared with the general ensemble learning methods, especially when the training set is small. In this paper, we take advantage of deep forest ensemble and introduce the Dense Adaptive Cascade Forest (daForest). Our model has a better performance than the original Cascade Forest with three major features: first, we apply SAMME.R boosting algorithm to improve the performance of the model. It guarantees the improvement as the number of layers increases. Second, our model connects each layer to the subsequent ones in a feed-forward fashion, which enhances the capability of the model to resist performance degeneration. Third, we add a hyper-parameters optimization layer before the first classification layer, making our model spend less time to set up and find the optimal hyper-parameters. Experimental results show that daForest performs significantly well, and in some cases, even outperforms neural networks and achieves state-of-the-art results.