Dense Adaptive Cascade Forest: A Densely Connected Deep Ensemble for Classification Problems
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Recent research has shown that deep ensemble for forest can achieve a huge increase in classification accuracy compared with the general ensemble learning method. Especially when there are only few training data. In this paper, we decide to take full advantage of this observation and introduce the Dense Adaptive Cascade Forest (daForest), which has better performance than the original one named Cascade Forest. And it is particularly noteworthy that daForest has a powerful ability to handle high-dimensional sparse data without any preprocessing on raw data like PCA or any other dimensional reduction methods. Our model is distinguished by three major features: the first feature is the combination of the SAMME.R boosting algorithm in the model, boosting gives the model the ability to continuously improve as the number of layer increases, which is not possible in stacking model or plain cascade forest. The second feature is our model connects each layer to its subsequent layers in a feed-forward fashion, to some extent this structure enhances the ability of the model to resist degeneration. When number of layers goes up, accuracy of model goes up a little in the first few layers then drop down quickly, we call this phenomenon degeneration in training stacking model. The third feature is that we add a hyper-parameter optimization layer before the first classification layer in the proposed deep model, which can search for the optimal hyper-parameter and set up the model in a brief period and nearly halve the training time without having too much impact on the final performance. Experimental results show that daForest performs particularly well on both high-dimensional low-order features and low-dimensional high-order features, and in some cases, even better than neural networks and achieves state-of-the-art results.
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