تقرير
Interpretable Graph Neural Networks for Heterogeneous Tabular Data
| العنوان: | Interpretable Graph Neural Networks for Heterogeneous Tabular Data |
|---|---|
| المؤلفون: | Alkhatib, Amr, Boström, Henrik |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | Many machine learning algorithms for tabular data produce black-box models, which prevent users from understanding the rationale behind the model predictions. In their unconstrained form, graph neural networks fall into this category, and they have further limited abilities to handle heterogeneous data. To overcome these limitations, an approach is proposed, called IGNH (Interpretable Graph Neural Network for Heterogeneous tabular data), which handles both categorical and numerical features, while constraining the learning process to generate exact feature attributions together with the predictions. A large-scale empirical investigation is presented, showing that the feature attributions provided by IGNH align with Shapley values that are computed post hoc. Furthermore, the results show that IGNH outperforms two powerful machine learning algorithms for tabular data, Random Forests and TabNet, while reaching a similar level of performance as XGBoost. Comment: Accepted at 27th International Conference on Discovery Science 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2408.07661 |
| رقم الأكسشن: | edsarx.2408.07661 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |