Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes
Journal:
arXiv
Published Date:
May 9, 2025
Abstract
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 method
outperforms vanilla KAN on a challenging blood cell classification dataset,
achieving higher accuracy under 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 integration of
ECOC with KAN for enhancing multi-class medical image classification
performance.
