Kolmogorov-Arnold Networks (KANs) have very recently been introduced into the world of machine learning, quickly capturing the attention of the entire community. However, KANs have mostly been tested for approximating complex functions or processing synthetic data, while a test on real-world tabular datasets is currently lacking. In this paper, we present a benchmarking study comparing KANs and Multi-Layer Perceptrons (MLPs) on tabular datasets. The study evaluates task performance and training times. From the results obtained on the various datasets, KANs demonstrate superior or comparable accuracy and F1 scores, excelling particularly in datasets with numerous instances, suggesting robust handling of complex data. We also highlight that this performance improvement of KANs comes with a higher computational cost when compared to MLPs of comparable sizes.

翻译：Kolmogorov-Arnold 网络（KANs）最近被引入机器学习领域，迅速引起了整个社区的关注。然而，KANs 目前主要被用于逼近复杂函数或处理合成数据，而在真实世界表格数据集上的测试尚属空白。本文提出了一项基准测试研究，在多个表格数据集上比较了 KANs 与多层感知机（MLPs）的性能。该研究评估了任务表现和训练时间。基于在不同数据集上获得的结果，KANs 展现出优于或与 MLPs 相当的准确率和 F1 分数，尤其在样本数量众多的数据集上表现突出，这表明其能稳健地处理复杂数据。我们也指出，与规模相当的 MLPs 相比，KANs 的这种性能提升伴随着更高的计算成本。