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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Deep Neural Network Initialization With Decision Trees
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Deep Neural Network Initialization With Decision Trees

机译：使用决策树进行深度神经网络初始化

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摘要

In this paper, a novel, automated process for constructing and initializing deep feedforward neural networks based on decision trees is presented. The proposed algorithm maps a collection of decision trees trained on the data into a collection of initialized neural networks with the structures of the networks determined by the structures of the trees. The tree-informed initialization acts as a warm-start to the neural network training process, resulting in efficiently trained, accurate networks. These models, referred to as "deep jointly informed neural networks" (DJINN), demonstrate high predictive performance for a variety of regression and classification data sets and display comparable performance to Bayesian hyperparameter optimization at a lower computational cost. By combining the user-friendly features of decision tree models with the flexibility and scalability of deep neural networks, DJINN is an attractive algorithm for training predictive models on a wide range of complex data sets.
机译：在本文中，提出了一种新颖的，基于决策树的构造和初始化深前馈神经网络的自动化过程。所提出的算法将经过数据训练的决策树集合映射到初始化的神经网络集合中，该神经网络具有由树的结构确定的网络结构。树状信息的初始化是神经网络训练过程的热启动，从而可以有效地训练出准确的网络。这些模型称为“深度联合信息神经网络”（DJINN），对各种回归和分类数据集显示出较高的预测性能，并以较低的计算成本显示了与贝叶斯超参数优化相当的性能。通过将决策树模型的用户友好功能与深度神经网络的灵活性和可伸缩性相结合，DJINN是一种用于在各种复杂数据集上训练预测模型的有吸引力的算法。

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