Extracting spatial-temporal knowledge from data is useful in many applications. It is important that the obtained knowledge is human-interpretable and amenable to formal analysis. In this paper, we propose a method that trains neural networks to learn spatial-temporal properties in the form of weighted graph-based signal temporal logic (wGSTL) formulas. For learning wGSTL formulas, we introduce a flexible wGSTL formula structure in which the user's preference can be applied in the inferred wGSTL formulas. In the proposed framework, each neuron of the neural networks corresponds to a subformula in a flexible wGSTL formula structure. We initially train a neural network to learn the wGSTL operators and then train a second neural network to learn the parameters in a flexible wGSTL formula structure. We use a COVID-19 dataset and a rain prediction dataset to evaluate the performance of the proposed framework and algorithms. We compare the performance of the proposed framework with three baseline classification methods including K-nearest neighbors, decision trees, support vector machine, and artificial neural networks. The classification accuracy obtained by the proposed framework is comparable with the baseline classification methods.
翻译:从数据中提取时空知识在许多应用中是有用的。 重要的是,获得的知识是人类可解释的,并且可以进行正式分析。 在本文中,我们提出一种方法,用于培训神经网络,以加权图形基信号时间逻辑(wGSTL)公式的形式学习空间时空特性。为了学习 wGSTL 公式,我们引入了一个灵活的 wGSTL 公式结构,在其中用户的偏好可以应用在推算的 wGSTL 公式中。在拟议框架中,神经网络的每个神经网络的每个神经都与灵活WGSTL 公式结构中的子形式相对应。我们最初培训一个神经网络,以学习WGSTL操作者,然后培训第二个神经网络,以学习灵活 WGSTL 公式结构中的参数。我们使用COVID-19 数据集和一个雨量预测数据集来评价拟议框架和算法的性能。我们将拟议框架的性能与三个基线分类方法进行了比较,包括K-近距离邻、决定树、支持矢量机和人造神经网络。 通过拟议的基本分类框架获得的精确性框架。