🤖 AI Summary
This work addresses the limited interpretability of artificial neural networks despite their high predictive accuracy. To bridge this gap, the authors propose a novel approach that partitions ReLU-based neural networks into individual units and represents them as three-dimensional bit tensors. For the first time, ternary formal concept analysis is introduced to extract symbolic logical rules from these tensors, which preserve the original classification performance. These rules are then organized into a human-readable logical decision tree. The method provides a transparent representation of internal attribute interactions within the network, significantly enhancing model interpretability without compromising accuracy. This study thus offers a new pathway toward symbolic interpretation of neural networks, combining the strengths of connectionist models with the clarity of symbolic reasoning.
📝 Abstract
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.