  Domain generalization is a popular machine learning technique that enables
models to perform well on the unseen target domain, by learning from multiple
source domains. Domain generalization is useful in cases where data is limited,
difficult, or expensive to collect, such as in object recognition and
biomedicine. In this paper, we propose a novel domain generalization algorithm
called "meta-forests", which builds upon the basic random forests model by
incorporating the meta-learning strategy and maximum mean discrepancy measure.
The aim of meta-forests is to enhance the generalization ability of classifiers
by reducing the correlation among trees and increasing their strength. More
specifically, meta-forests conducts meta-learning optimization during each
meta-task, while also utilizing the maximum mean discrepancy as a
regularization term to penalize poor generalization performance in the
meta-test process. To evaluate the effectiveness of our algorithm, we test it
on two publicly object recognition datasets and a glucose monitoring dataset
that we have used in a previous study. Our results show that meta-forests
outperforms state-of-the-art approaches in terms of generalization performance
on both object recognition and glucose monitoring datasets.
