发表机构
University of Information Technology, Ho Chi Minh City, Vietnam; Vietnam National University Ho Chi Minh City, Ho Chi Minh City, Vietnam（胡志明市信息技术大学; 越南国立大学胡志明市分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文在ExtraSensory数据集上评估多种KAN变体用于个人情境识别，发现其性能优于MLP且与TabM相当，但训练时间更长，表明KANs有潜力但计算效率待提升。
AI 中文摘要
近年来，Kolmogorov-Arnold网络（KANs）引起了越来越多的关注，其应用遍及广泛的AI任务。在本文中，我们在ExtraSensory数据集上评估了几种KAN变体用于个人情境识别，并将它们与多层感知器（MLP）和TabM进行了比较。我们使用五个用户折（fold）和每折三个随机种子进行了主要实验，并报告了平均Macro-F1、Micro-F1和训练时间。我们还对网格大小、网格数量和数据归一化进行了浅层消融研究，以考察它们对KAN性能的影响。结果表明，所有评估的KAN变体在Macro-F1和Micro-F1方面显著优于MLP，并达到了与TabM相当的性能。然而，KAN变体通常需要更长的训练时间，而TabM在预测性能和训练效率之间提供了更有利的平衡。这些结果表明，KANs在个人情境识别方面具有潜力，但其计算效率仍然是一个重要挑战。我们的源代码公开可用，网址为：this https URL。
英文摘要
Kolmogorov--Arnold Networks (KANs) have attracted increasing attention in recent years, with applications across a wide range of AI tasks. In this paper, we evaluate several KAN variants on the ExtraSensory dataset for personal context recognition and compare them with a multilayer perceptron (MLP) and TabM. We conduct the main experiments using five user folds and three random seeds per fold and report the average Macro-F1, Micro-F1, and training time. We also perform shallow ablation studies on grid size, the number of grids, and data normalization to examine their effects on KAN performance. The results show that all evaluated KAN variants significantly outperform MLP in terms of Macro-F1 and Micro-F1 and achieve performance comparable to TabM. However, KAN variants generally require more training time, while TabM provides a more favorable balance between predictive performance and training efficiency. These results suggest that KANs are promising for personal context recognition, while their computational efficiency remains an important challenge. Our source code is publicly available at: https://github.com/hoangthangta/ExtraSensory-KANs.
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