Paper ID: 2409.08806

TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer

Weihao Gao, Zheng Gong, Zhuo Deng, Fuju Rong, Chucheng Chen, Lan Ma

Tabular data is the most common type of data in real-life scenarios. In this study, we propose the TabKANet model for tabular data modeling, which targets the bottlenecks in learning from numerical content. We constructed a Kolmogorov-Arnold Network (KAN) based Numerical Embedding Module and unified numerical and categorical features encoding within a Transformer architecture. TabKANet has demonstrated stable and significantly superior performance compared to Neural Networks (NNs) across multiple public datasets in binary classification, multi-class classification, and regression tasks. Its performance is comparable to or surpasses that of Gradient Boosted Decision Tree models (GBDTs). Our code is publicly available on GitHub: this https URL.

Submitted: Sep 13, 2024