Dense Adaptive Cascade Forest: A Self Adaptive Deep Ensemble for Classification Problems
This work addresses classification problems, particularly with limited data, but is incremental as it builds on existing deep forest ensemble methods.
The paper tackles improving classification accuracy with small training sets by introducing Dense Adaptive Cascade Forest (daForest), which outperforms the original Cascade Forest and in some cases achieves state-of-the-art results, even surpassing neural networks.
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.