Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866912473803653120 |
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| author | Vahedifar, Mohammad Ali Akhtarshenas, Azim Rafatpanah, Mohammad Mohammadi Sabbaghian, Maryam |
| author_facet | Vahedifar, Mohammad Ali Akhtarshenas, Azim Rafatpanah, Mohammad Mohammadi Sabbaghian, Maryam |
| contents | The K-Nearest Neighbors (KNN) algorithm is widely used for classification and regression; however, it suffers from limitations, including the equal treatment of all samples. We propose Information-Modified KNN (IM-KNN), a novel approach that leverages Mutual Information ($I$) and Shapley values to assign weighted values to neighbors, thereby bridging the gap in treating all samples with the same value and weight. On average, IM-KNN improves the accuracy, precision, and recall of traditional KNN by 16.80%, 17.08%, and 16.98%, respectively, across 12 benchmark datasets. Experiments on four large-scale datasets further highlight IM-KNN's robustness to noise, imbalanced data, and skewed distributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01991 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors Vahedifar, Mohammad Ali Akhtarshenas, Azim Rafatpanah, Mohammad Mohammadi Sabbaghian, Maryam Machine Learning Information Theory The K-Nearest Neighbors (KNN) algorithm is widely used for classification and regression; however, it suffers from limitations, including the equal treatment of all samples. We propose Information-Modified KNN (IM-KNN), a novel approach that leverages Mutual Information ($I$) and Shapley values to assign weighted values to neighbors, thereby bridging the gap in treating all samples with the same value and weight. On average, IM-KNN improves the accuracy, precision, and recall of traditional KNN by 16.80%, 17.08%, and 16.98%, respectively, across 12 benchmark datasets. Experiments on four large-scale datasets further highlight IM-KNN's robustness to noise, imbalanced data, and skewed distributions. |
| title | Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors |
| topic | Machine Learning Information Theory |
| url | https://arxiv.org/abs/2312.01991 |