On the Learning with Augmented Class via Forests

• URL: http://arxiv.org/abs/2505.09294v2
• Date: Mon, 14 Jul 2025 08:29:44 GMT
• Title: On the Learning with Augmented Class via Forests
• Authors: Fan Xu, Wuyang Chen, Wei Gao,
• Abstract summary: We focus on learning with augmented class via forests, where an augmented class may appear in testing data yet not in training data.<n>We develop the Learning with Augmented Class via Forests approach, which constructs shallow forests according to the augmented Gini impurity.<n>We also develop deep neural forests via an optimization objective based on our augmented Gini impurity.
• Score: 17.606415934443554
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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