Beyond Tree Models: A Hybrid Model of KAN and gMLP for Large-Scale Financial Tabular Data
It provides a practical solution for handling large-scale numerical tabular data in financial and industrial applications, though it appears incremental as it builds on existing neural network architectures.
The paper tackles the challenges of large-scale financial tabular data, such as data heterogeneity and computational issues with tree models, by introducing TKGMLP, a hybrid model combining Kolmogorov Arnold Networks and Gated Multilayer Perceptron, which achieves state-of-the-art results on a credit scoring dataset and shows improved scalability with increasing dataset size.
Tabular data plays a critical role in real-world financial scenarios. Traditionally, tree models have dominated in handling tabular data. However, financial datasets in the industry often encounter some challenges, such as data heterogeneity, the predominance of numerical features and the large scale of the data, which can range from tens of millions to hundreds of millions of records. These challenges can lead to significant memory and computational issues when using tree-based models. Consequently, there is a growing need for neural network-based solutions that can outperform these models. In this paper, we introduce TKGMLP, an hybrid network for tabular data that combines shallow Kolmogorov Arnold Networks with Gated Multilayer Perceptron. This model leverages the strengths of both architectures to improve performance and scalability. We validate TKGMLP on a real-world credit scoring dataset, where it achieves state-of-the-art results and outperforms current benchmarks. Furthermore, our findings demonstrate that the model continues to improve as the dataset size increases, making it highly scalable. Additionally, we propose a novel feature encoding method for numerical data, specifically designed to address the predominance of numerical features in financial datasets. The integration of this feature encoding method within TKGMLP significantly improves prediction accuracy. This research not only advances table prediction technology but also offers a practical and effective solution for handling large-scale numerical tabular data in various industrial applications.