Improvement of the Deep Forest Classifier by a Set of Neural Networks

Lev V. Utkin, Kirill D. Zhuk


A Neural Random Forest (NeuRF) and a Neural Deep Forest (NeuDF) as classification algorithms, which combine an ensemble of decision trees and neural networks, are proposed in the paper. The main idea underlying NeuRF is to combine the class probability distributions produced by decision trees by means of a set of neural networks with shared parameters. The networks are trained in accordance with a loss function which measures the classification error. Every neural network can be viewed as a non-linear function of probabilities of a class. NeuDF is a modification of the Deep Forest or gcForest proposed by Zhou and Feng, using NeuRFs. The numerical experiments illustrate the outperformance of NeuDF and show that the NeuRF is comparable with the random forest

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