论文标题
部分可观测时空混沌系统的无模型预测
DBCal: Density Based Calibration of classifier predictions for uncertainty quantification
论文作者
论文摘要
在科学领域和应用之间，测量机器学习方法预测的不确定性至关重要。据我们所知，我们介绍了第一个量化分类器预测不确定性的技术，并说明了分类器的信念和绩效。我们证明，我们的方法通过显示出二进制分类器的预期校准误差小于0.2％，而在具有极端类失衡的语义细分网络上显示的预期校准误差小于0.2％，从而提供了对两个神经网络的输出是正确的概率的准确估计。我们从经验上表明，我们方法返回的不确定性是对分类器预测正确的概率的准确度量，因此在不确定性传播中具有广泛的效用。
Measurement of uncertainty of predictions from machine learning methods is important across scientific domains and applications. We present, to our knowledge, the first such technique that quantifies the uncertainty of predictions from a classifier and accounts for both the classifier's belief and performance. We prove that our method provides an accurate estimate of the probability that the outputs of two neural networks are correct by showing an expected calibration error of less than 0.2% on a binary classifier, and less than 3% on a semantic segmentation network with extreme class imbalance. We empirically show that the uncertainty returned by our method is an accurate measurement of the probability that the classifier's prediction is correct and, therefore has broad utility in uncertainty propagation.
