Denoising random forests

TL;DR

This paper introduces denoising random forests that utilize denoising autoencoders on traversal path indicators to improve robustness against noisy test samples, enhancing estimation accuracy.

Contribution

It presents a novel approach combining denoising autoencoders with random forests to mitigate noise effects in test samples, which is a new method in this domain.

Findings

Improved robustness against noisy test samples.

Enhanced estimation accuracy in noisy conditions.

Effective identification of incorrect decision nodes.

Abstract

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.