Kolmogorov-Arnold Networks (KAN) offer universal function approximation using univariate spline compositions without nonlinear activations. In this work, we integrate Error-Correcting Output Codes (ECOC) into the KAN framework to transform multi-class classification into multiple binary tasks, improving robustness via Hamming distance decoding. Our proposed KAN with ECOC framework outperforms vanilla KAN on a challenging blood cell classification dataset, achieving higher accuracy across diverse hyperparameter settings. Ablation studies further confirm that ECOC consistently enhances performance across FastKAN and FasterKAN variants. These results demonstrate that ECOC integration significantly boosts KAN generalizability in critical healthcare AI applications. To the best of our knowledge, this is the first work of ECOC with KAN for enhancing multi-class medical image classification performance.

翻译：科尔莫戈罗夫-阿诺德网络（KAN）通过一元样条组合实现通用函数逼近，无需非线性激活函数。本研究将纠错输出码（ECOC）集成至KAN框架，将多分类任务转化为多个二分类任务，并通过汉明距离解码提升模型鲁棒性。在具有挑战性的血细胞分类数据集上，我们提出的KAN-ECOC框架优于原始KAN模型，在不同超参数设置下均取得更高准确率。消融实验进一步证实ECOC能持续提升FastKAN与FasterKAN变体的性能。这些结果表明，ECOC集成能显著增强KAN在关键医疗AI应用中的泛化能力。据我们所知，这是首次将ECOC与KAN结合以提升多类别医学图像分类性能的研究。