TabKANet: Tabular Data Modeling with Kolmogorov-Arnold Network and Transformer
Keywords: Tabular Data Modeling; Kolmogorov-Arnold Network; Numerical Feature Embedding
TL;DR: We introduce TabKANet, a novel model that leverages a KAN-based Numerical embedding module and Transformer to overcome neural networks' limitations in tabular data. It achieves performance comparable to or exceeding GBDT models on various datasets.
Abstract: 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.
Primary Area: foundation or frontier models, including LLMs
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Submission Number: 10773
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