Interpretable Graph Neural Networks for Tabular Data
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913466158153728 |
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| author | Alkhatib, Amr Ennadir, Sofiane Boström, Henrik Vazirgiannis, Michalis |
| author_facet | Alkhatib, Amr Ennadir, Sofiane Boström, Henrik Vazirgiannis, Michalis |
| contents | Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to produce an interpretable model, where the model shows how the predictions are exactly computed from the original input features. A large-scale empirical investigation is presented, showing that IGNNet is performing on par with state-of-the-art machine-learning algorithms that target tabular data, including XGBoost, Random Forests, and TabNet. At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_08945 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Interpretable Graph Neural Networks for Tabular Data Alkhatib, Amr Ennadir, Sofiane Boström, Henrik Vazirgiannis, Michalis Machine Learning Artificial Intelligence Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to produce an interpretable model, where the model shows how the predictions are exactly computed from the original input features. A large-scale empirical investigation is presented, showing that IGNNet is performing on par with state-of-the-art machine-learning algorithms that target tabular data, including XGBoost, Random Forests, and TabNet. At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead. |
| title | Interpretable Graph Neural Networks for Tabular Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2308.08945 |