当前位置: X-MOL 学术arXiv.cs.AI › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Towards Robust Classification with Deep Generative Forests
arXiv - CS - Artificial Intelligence Pub Date : 2020-07-11 , DOI: arxiv-2007.05721
Alvaro H. C. Correia, Robert Peharz, Cassio de Campos

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.

中文翻译:

借助深层可造林实现稳健的分类

决策树和随机森林是使用最广泛的机器学习模型之一,通常在表格,与领域无关的数据集中实现最先进的性能。但是,由于它们是主要的区分性模型,它们缺乏可操纵预测不确定性的原则方法。在本文中,我们利用生成森林(GeFs),这是一类最新的深度概率模型,它通过将随机森林扩展为代表特征空间上完整联合分布的生成模型来解决这些问题。我们证明了GeF是可识别不确定性的分类器,能够测量每个预测的鲁棒性以及检测分布不均的样本。
更新日期:2020-07-14
down
wechat
bug