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arXiv · 1911.10829

Neural Random Forest Imitation

Christoph Reinders·Bodo Rosenhahn
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Abstract

We present Neural Random Forest Imitation - a novel approach for transforming random forests into neural networks. Existing methods propose a direct mapping and produce very inefficient architectures. In this work, we introduce an imitation learning approach by generating training data from a random forest and learning a neural network that imitates its behavior. This implicit transformation creates very efficient neural networks that learn the decision boundaries of a random forest. The generated model is differentiable, can be used as a warm start for fine-tuning, and enables end-to-end optimization. Experiments on several real-world benchmark datasets demonstrate superior performance, especially when training with very few training examples. Compared to state-of-the-art methods, we significantly reduce the number of network parameters while achieving the same or even improved accuracy due to better generalization.

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Christoph Reinders, Bodo Rosenhahn. 2019-11-25. Neural Random Forest Imitation. https://doi.org/10.1007/978-3-031-35374-1_5

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