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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/2401.16294 |
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| _version_ | 1866913213950459904 |
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| author | Konstantinov, Andrei V. Kozlov, Boris V. Kirpichenko, Stanislav R. Utkin, Lev V. |
| author_facet | Konstantinov, Andrei V. Kozlov, Boris V. Kirpichenko, Stanislav R. Utkin, Lev V. |
| contents | A new approach to the local and global explanation is proposed. It is based on selecting a convex hull constructed for the finite number of points around an explained instance. The convex hull allows us to consider a dual representation of instances in the form of convex combinations of extreme points of a produced polytope. Instead of perturbing new instances in the Euclidean feature space, vectors of convex combination coefficients are uniformly generated from the unit simplex, and they form a new dual dataset. A dual linear surrogate model is trained on the dual dataset. The explanation feature importance values are computed by means of simple matrix calculations. The approach can be regarded as a modification of the well-known model LIME. The dual representation inherently allows us to get the example-based explanation. The neural additive model is also considered as a tool for implementing the example-based explanation approach. Many numerical experiments with real datasets are performed for studying the approach. The code of proposed algorithms is available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_16294 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dual feature-based and example-based explanation methods Konstantinov, Andrei V. Kozlov, Boris V. Kirpichenko, Stanislav R. Utkin, Lev V. Machine Learning Artificial Intelligence A new approach to the local and global explanation is proposed. It is based on selecting a convex hull constructed for the finite number of points around an explained instance. The convex hull allows us to consider a dual representation of instances in the form of convex combinations of extreme points of a produced polytope. Instead of perturbing new instances in the Euclidean feature space, vectors of convex combination coefficients are uniformly generated from the unit simplex, and they form a new dual dataset. A dual linear surrogate model is trained on the dual dataset. The explanation feature importance values are computed by means of simple matrix calculations. The approach can be regarded as a modification of the well-known model LIME. The dual representation inherently allows us to get the example-based explanation. The neural additive model is also considered as a tool for implementing the example-based explanation approach. Many numerical experiments with real datasets are performed for studying the approach. The code of proposed algorithms is available. |
| title | Dual feature-based and example-based explanation methods |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.16294 |