论文标题
用深层生成森林进行稳健的分类
Towards Robust Classification with Deep Generative Forests
论文作者
论文摘要
决策树和随机森林是使用最广泛的机器学习模型之一，并且通常在表格，域形信息数据集中实现最先进的性能。尽管如此，作为歧视模型，它们缺乏原则性的方法来操纵预测的不确定性。在本文中，我们利用生成森林（GEFS），这是一种最近的深层概率模型，通过将随机森林扩展到代表特征空间上完整的关节分布的生成模型来解决这些问题。我们证明GEF是不确定性感知的分类器，能够测量每个预测的鲁棒性以及检测分布外样品。
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets. Nonetheless, being primarily discriminative models they lack principled methods to manipulate the uncertainty of predictions. In this paper, we exploit Generative Forests (GeFs), a recent class of deep probabilistic models that addresses these issues by extending Random Forests to generative models representing the full joint distribution over the feature space. We demonstrate that GeFs are uncertainty-aware classifiers, capable of measuring the robustness of each prediction as well as detecting out-of-distribution samples.
