An artificial neural network (ANN) is a numerical method used to solve complex classification problems. Due to its high classification power, the ANN method often outperforms other classification methods in terms of accuracy. However, an ANN model lacks interpretability compared to methods that use the symbolic paradigm. Our idea is to derive a symbolic representation from a simple ANN model trained on minterm values of input objects. Based on ReLU nodes, the ANN model is partitioned into cells. We convert the ANN model into a cell-based, three-dimensional bit tensor. The theory of Formal Concept Analysis applied to the tensor yields concepts that are represented as logic trees, expressing interpretable attribute interactions. Their evaluations preserve the classification power of the initial ANN model.

翻译：人工神经网络（ANN）是一种用于解决复杂分类问题的数值方法。由于其强大的分类能力，ANN方法在准确率方面通常优于其他分类方法。然而，与采用符号范式的方法相比，ANN模型缺乏可解释性。我们的思路是从一个在输入对象的最小项值上训练的简单ANN模型中推导出符号表示。基于ReLU节点，该ANN模型被划分为若干单元。我们将ANN模型转换为一个基于单元的三维比特张量。应用于该张量的形式概念分析理论产生出以逻辑树表示的概念，这些逻辑树表达了可解释的属性交互作用。对这些概念的评估保留了初始ANN模型的分类能力。