论文标题
犹豫不决树：量化不确定性下的基于论证的推理
Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty
论文作者
论文摘要
在现实世界中，使用机器学习系统通常可能是有问题的，使用莫名其妙的黑盒模型，假定的不完善测量的确定性或提供单个分类而不是概率分布。 本文介绍了犹豫不决的树，对在不确定性下学习的决策树进行了修改，可以在不确定性下执行推理，在可能的标签上提供强大的分布，并可以将其分解为一组逻辑论证，以用于其他推理系统。
Using Machine Learning systems in the real world can often be problematic, with inexplicable black-box models, the assumed certainty of imperfect measurements, or providing a single classification instead of a probability distribution. This paper introduces Indecision Trees, a modification to Decision Trees which learn under uncertainty, can perform inference under uncertainty, provide a robust distribution over the possible labels, and can be disassembled into a set of logical arguments for use in other reasoning systems.
