Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice
Year: 2024
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Lu, Yang, Zhan, Felix
Institutions: University of Cincinnati, Stanford University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403160585