  Random forest and deep neural network are two schools of effective
classification methods in machine learning. While the random forest is robust
irrespective of the data domain, the deep neural network has advantages in
handling high dimensional data. In view that a differentiable neural decision
forest can be added to the neural network to fully exploit the benefits of both
models, in our work, we further combine convolutional autoencoder with neural
decision forest, where autoencoder has its advantages in finding the hidden
representations of the input data. We develop a gradient boost module and embed
it into the proposed convolutional autoencoder with neural decision forest to
improve the performance. The idea of gradient boost is to learn and use the
residual in the prediction. In addition, we design a structure to learn the
parameters of the neural decision forest and gradient boost module at
contiguous steps. The extensive experiments on several public datasets
demonstrate that our proposed model achieves good efficiency and prediction
performance compared with a series of baseline methods.
