Meta-forests：基于元学习的随机森林领域泛化
Meta-forests: Domain generalization on random forests with meta-learning
AI总结：
本文提出名为meta-forests的领域泛化算法，结合元学习与最大均值差异优化随机森林，降低树间相关性并增强强度，在物体识别与葡萄糖监测数据集上泛化性能优于现有方法。
摘要：
领域泛化是一种流行的机器学习技术，通过从多个源领域学习，使模型在未见的目标领域上表现良好。在数据收集受限、困难或昂贵的场景下，如物体识别和生物医学，领域泛化非常有用。本文提出了一种名为“meta-forests”的新型领域泛化算法，它在基础随机森林模型上结合了元学习策略和最大均值差异度量。meta-forests旨在通过降低树间相关性并增强其强度来提升分类器的泛化能力。具体而言，meta-forests在每个元任务中进行元学习优化，同时利用最大均值差异作为正则化项，惩罚元测试过程中泛化性能不佳的情况。为评估算法有效性，我们在两个公开的物体识别数据集和我们先前研究中使用的一个葡萄糖监测数据集上进行了测试。结果表明，meta-forests在物体识别和葡萄糖监测数据集上的泛化性能均优于现有先进方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
