Invariant Random Forest : Tree-Based Model Solution for OOD Generalization

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationThe Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024)
Publication statusPublished - Feb 2024

Conference

Title38th Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI-24)
LocationVancouver Convention Center
PlaceCanada
CityVancouver
Period20 - 27 February 2024

Abstract

Out-Of-Distribution (OOD) generalization is an essential topic in machine learning. However, recent research is only focusing on the corresponding methods for neural networks. This paper introduces a novel and effective solution for OOD generalization of decision tree models, named Invariant Decision Tree (IDT). IDT enforces a penalty term with regard to the unstable/varying behavior of a split across different environments during the growth of the tree. Its ensemble version, the Invariant Random Forest (IRF), is constructed. Our proposed method is motivated by a theoretical result under mild conditions, and validated by numerical tests with both synthetic and real datasets. The superior performance compared to non-OOD tree models implies that considering OOD generalization for tree models is absolutely necessary and should be given more attention.

Bibliographic Note

Since this conference is yet to commence, the information for this record is subject to revision.

Citation Format(s)

Invariant Random Forest: Tree-Based Model Solution for OOD Generalization. / LIAO, Yufan; WU, Qi; YAN, Xing.
The Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024). 2024.

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review