# Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes

Source: [Improving Generalizability of Kolmogorov-Arnold Networks via Error-Correcting Output Codes](<https://www.sota2.com/research/paper/68fa7b681e209131cea883c9>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T07:02:34.980Z

Authors: Youngjoon Lee, Jinu Gong, Joonhyuk Kang

Published: 2025-05-09T05:31:10.000Z

## 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 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2505.05798>)

[PDF](<https://arxiv.org/pdf/2505.05798.pdf>)