  This paper proposes a novel type of random forests called a denoising random
forests that are robust against noises contained in test samples. Such
noise-corrupted samples cause serious damage to the estimation performances of
random forests, since unexpected child nodes are often selected and the leaf
nodes that the input sample reaches are sometimes far from those for a clean
sample. Our main idea for tackling this problem originates from a binary
indicator vector that encodes a traversal path of a sample in the forest. Our
proposed method effectively employs this vector by introducing denoising
autoencoders into random forests. A denoising autoencoder can be trained with
indicator vectors produced from clean and noisy input samples, and non-leaf
nodes where incorrect decisions are made can be identified by comparing the
input and output of the trained denoising autoencoder. Multiple traversal paths
with respect to the nodes with incorrect decisions caused by the noises can
then be considered for the estimation.
