The uncertainty measurement of classifiers' predictions is especially
important in applications such as medical diagnoses that need to ensure limited
human resources can focus on the most uncertain predictions returned by machine
learning models. However, few existing uncertainty models attempt to improve
overall prediction accuracy where human resources are involved in the text
classification task. In this paper, we propose a novel neural-network-based
model that applies a new dropout-entropy method for uncertainty measurement. We
also design a metric learning method on feature representations, which can
boost the performance of dropout-based uncertainty methods with smaller
prediction variance in accurate prediction trials. Extensive experiments on
real-world data sets demonstrate that our method can achieve a considerable
improvement in overall prediction accuracy compared to existing approaches. In
particular, our model improved the accuracy from 0.78 to 0.92 when 30\% of the
most uncertain predictions were handed over to human experts in "20NewsGroup"
data.

本篇论文提出了一种基于神经网络和新型 dropout - 熵测量方法的模型以及基于特征表示的度量学习方法，可以更精确地预测，提高分类器的预测准确性，尤其在医学诊断等需要确定不确定预测的领域有广泛的应用。