Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice
<|reference_start|>Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice: This study evaluates the applicability of Kolmogorov-Arnold Networks (KAN) in fraud detection, finding that their effectiveness is context-dependent. We propose a quick decision rule using Principal Component Analysis (PCA) to assess the suitability of KAN: if data can be effectively separated in two dimensions using splines, KAN may outperform traditional models; otherwise, other methods could be more appropriate. We also introduce a heuristic approach to hyperparameter tuning, significantly reducing computational costs. These findings suggest that while KAN has potential, its use should be guided by data-specific assessments.<|reference_end|>
@article{lu2024kolmogorov,
    title={Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between
  Theory and Practice},
    author={Yang Lu and Felix Zhan},
    journal={arXiv preprint arXiv:2408.10263},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10263},
    primaryClass={cs.LG cs.NE}
}