Kolmogorov Arnold Networks (KAN) are highly efficient in inference and can handle complex patterns once trained, making them desirable for production environments and ensuring a fast service experience in the finance and electronic shopping industries. However, we found that KAN, in general, is not suitable for fraud detection problems. We also discovered a quick method to determine whether a problem is solvable by KAN: if the data can be effectively separated using spline interpolation with varying intervals after applying Principal Component Analysis (PCA) to reduce the data dimensions to two, KAN can outperform most machine learning algorithms. Otherwise, it indicates KAN may not solve the problem effectively compared to other machine learning algorithms. We also propose a heuristic approach for selecting the appropriate hyperparameters for KAN to significantly accelerate training time compared to grid search hyperparameter tuning, which usually takes a month for a comprehensive grid search. Specifically, the width parameter should generally follow a pyramid structure, allowing efficient spline mixing, and k should be fixed at 15, with the grid number fixed at 5. This streamlined approach minimizes the number of evaluations required, significantly speeding up the hyperparameter tuning process while still achieving robust performance metrics.

翻译：Kolmogorov-Arnold网络（KAN）推理效率极高，一经训练便能处理复杂模式，这使其在生产环境中极具吸引力，并能确保金融和电子购物行业提供快速的服务体验。然而，我们发现KAN总体上并不适用于欺诈检测问题。我们还发现了一种快速判断问题是否可由KAN解决的方法：如果在对数据进行主成分分析（PCA）降维至二维后，能够通过变区间样条插值有效分离数据，则KAN的表现可能优于大多数机器学习算法。反之，则表明与其他机器学习算法相比，KAN可能无法有效解决该问题。此外，我们提出了一种启发式方法，用于为KAN选择合适的超参数，与通常需要一个月时间进行全面网格搜索的超参数调优相比，该方法能显著加速训练过程。具体而言，宽度参数通常应遵循金字塔结构，以实现高效的样条混合；参数k应固定为15，网格数固定为5。这种简化的方法最大限度地减少了所需的评估次数，在仍能获得稳健性能指标的同时，显著加快了超参数调优过程。