{"type":"ScholarlyArticle","title":"On the Learning with Augmented Class via Forests","authors":[{"name":"Fan Xu","slug":"fan-xu","given":"Fan","family":"Xu","affiliation":""},{"name":"Wuyang Chen","slug":"wuyang-chen","given":"Wuyang","family":"Chen","affiliation":""},{"name":"Wei Gao","slug":"wei-gao","given":"Wei","family":"Gao","affiliation":""}],"abstract":"Decision trees and forests have achieved successes in various real applications, most working with all testing classes known in training data. In this work, we focus on learning with augmented class via forests, where an augmented class may appear in testing data yet not in training data. We incorporate information of augmented class into trees' splitting, that is, augmented Gini impurity, a new splitting criterion is introduced to exploit some unlabeled data from testing distribution. We then develop the Learning with Augmented Class via Forests (short for LACForest) approach, which constructs shallow forests according to the augmented Gini impurity and then splits forests with pseudo-labeled augmented instances for better performance. We also develop deep neural forests via an optimization objective based on our augmented Gini impurity, which essentially utilizes the representation power of neural networks for forests. Theoretically, we present the convergence analysis for our augmented Gini impurity, and we finally conduct experiments to evaluate our approaches. The code is available at https://github.com/nju-xuf/LACForest.","doi":"10.24963/ijcai.2025/748","url":"https://ijcai.org/proceedings/2025/748","pdf":"https://ijcai.org/proceedings/2025/0748.pdf","markdown":"https://ijcai.org/proceedings/2025/748.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/748","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Machine Learning","track":"Main Track","number":748,"pages":{"first":6723,"last":6731},"keywords":["Machine Learning: ML: Semi-supervised learning","Machine Learning: ML: Open-World/Open-Set/OOD Learning","Machine Learning: ML: Classification"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}