TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
<|reference_start|>TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer: 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: https://github.com/AI-thpremed/TabKANet.<|reference_end|>
@article{gao2024tabkanet:,
    title={TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and
  Transformer},
    author={Weihao Gao, Zheng Gong, Zhuo Deng, Fuju Rong, Chucheng Chen, Lan Ma},
    journal={arXiv preprint arXiv:2409.08806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08806},
    primaryClass={cs.LG cs.AI}
}