Interpretability Enhancement for Kolmogorov-Arnold Networks with Decision Trees in Power Systems

As artificial intelligence models are increasingly applied in high-risk domains, such as power systems, improving the interpretability of the model has become essential. Recently, Kolmogorov-Arnold networks (KAN) have attracted attention due to their enhanced transparency through learnable activation functions and visualizable structures. However, KAN still lack explicit decision rule representations, which hinders their applications in high-risk tasks. To overcome this limitation, this paper proposes a hybrid method that integrates KAN with decision trees to extract key features and analyze the decision rule. Experiments show that decision trees can approximate KAN behavior and quantify the importance of features. Using the pruning mechanism of KAN, we further evaluated the impact of selected features on model performance. The results suggest that the proposed method retains predictive accuracy while enhancing interpretability, offering a promising approach to understand deep learning models in power systems.