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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.00371 |
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| _version_ | 1866929428453392384 |
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| author | Hiraki, Kazuhiro Ishihara, Shinichi Shino, Junnosuke |
| author_facet | Hiraki, Kazuhiro Ishihara, Shinichi Shino, Junnosuke |
| contents | This study first derives a general and analytical expression of AFA (Additive Feature Attribution) in terms of the kernel in LIME (Local Interpretable Model-agnostic Explanations). Then, we propose some new AFAs that have appropriate properties of kernels or that coincide with the LS prenucleolus in cooperative game theory. We also revisit existing AFAs such as SHAP (SHapley Additive exPlanations) and re-examine the properties of their kernels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_00371 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Alternative Methods to SHAP Derived from Properties of Kernels: A Note on Theoretical Analysis Hiraki, Kazuhiro Ishihara, Shinichi Shino, Junnosuke Machine Learning This study first derives a general and analytical expression of AFA (Additive Feature Attribution) in terms of the kernel in LIME (Local Interpretable Model-agnostic Explanations). Then, we propose some new AFAs that have appropriate properties of kernels or that coincide with the LS prenucleolus in cooperative game theory. We also revisit existing AFAs such as SHAP (SHapley Additive exPlanations) and re-examine the properties of their kernels. |
| title | Alternative Methods to SHAP Derived from Properties of Kernels: A Note on Theoretical Analysis |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2406.00371 |