The recent decade has seen an enormous rise in the popularity of deep learning and neural networks. These algorithms have broken many previous records and achieved remarkable results. Their outstanding performance has significantly sped up the progress of AI, and so far various milestones have been achieved earlier than expected. However, in the case of relatively small datasets, the performance of Deep Neural Networks (DNN) may suffer from reduced accuracy compared to other Machine Learning models. Furthermore, it is difficult to construct prediction intervals or evaluate the uncertainty of predictions when dealing with regression tasks. In this paper, we propose an ensemble method that attempts to estimate the uncertainty of predictions, increase their accuracy and provide an interval for the expected variation. Compared with traditional DNNs that only provide a prediction, our proposed method can output a prediction interval by combining DNNs, extreme gradient boosting (XGBoost) and dissimilarity computation techniques. Albeit the simple design, this approach significantly increases accuracy on small datasets and does not introduce much complexity to the architecture of the neural network. The proposed method is tested on various datasets, and a significant improvement in the performance of the neural network model is seen. The model's prediction interval can include the ground truth value at an average rate of 71% and 78% across training sizes of 90% and 55%, respectively. Finally, we highlight other aspects and applications of the approach in experimental error estimation, and the application of transfer learning.
翻译:近十年来,深层学习和神经网络的受欢迎程度大幅上升。这些算法打破了许多先前的记录,取得了显著的成果。它们的杰出表现大大加快了AI的进展,迄今为止取得了比预期早得多的里程碑。然而,在相对较小的数据集中,深神经网络的性能可能因为与其他机器学习模型相比的精确度下降而受到影响。此外,在处理回归任务时很难建立预测间隔或评估预测的不确定性。在本文中,我们提出了一种共同的方法,试图估算预测的不确定性,提高预测的准确性,并为预期的变异提供一个间隔。与传统的DNNN(仅提供预测)相比,我们拟议的方法可以将DNN(D)、极梯度加速(XGBoost)和不相近的计算技术结合起来,从而产生一个预测间隔。尽管设计简单,这种方法大大提高了小型数据集的准确度,而且不会给神经网络的结构带来多大的复杂程度。拟议的方法在各种数据集中测试,提高了它们的准确性,并且为预期的期间距也大大改进了预期。我们所观察了98网络的模型和底值的模型。