提出新分裂准则，让随机森林应对训练中未见的测试类别。
On the Learning with Augmented Class via Forests
- 用增强版基尼不纯度指导树分裂，融合测试分布的无标签数据。
- 在多个数据集上显著提升对未知类别的识别准确率，最高达8.2%提升。
- 适合处理现实场景中类别动态扩展的机器学习任务。
决策树与随机森林在众多实际应用中表现优异，但通常假设测试类别均出现在训练数据中。本文研究了在训练中未出现但在测试中可能出现的新增类别问题，提出基于森林的增量学习方法LACForest。通过引入增强基尼不纯度（augmented Gini impurity），将测试分布中的无标签数据信息融入树的分裂过程，构建浅层森林，并利用伪标签的新增样本进一步优化模型性能。同时，基于该准则设计深度神经森林，结合神经网络的表征能力。理论分析证明了增强基尼不纯度的收敛性。实验验证了方法在多个数据集上的有效性，代码已开源：https://github.com/nju-xuf/LACForest。
原文摘要 · Abstract (English)
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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