Towards Robust Classification with Deep Generative Forests

2020-07-11 · 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.

📄 PDF Abstract BibTeX arXiv:2007.05721

Code (1)

AlCorreia/GeFs 공식 구현

Tasks

BIG-bench Machine Learning Classification General ClassificationRobust classification

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regression