Efficient Banzhaf-Based Data Valuation for k-Nearest Neighbors Classification
Summary: Establishes #P-hardness of Banzhaf valuation for kNN, then exploits neighborhood locality for exact dynamic programs: O(Wkn²) weighted and O(nk²) unweighted, plus efficient Monte Carlo estimators validated empirically. (summarized by gpt-5.6-luna on Aug 17 2026)
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Authors
- 1. Guangyi Zhang (Shenzhen University)
- 2. Lutz Oettershagen (University of Liverpool)
- 3. Lixu Wang (Nanyang Technological University)
- 4. Aristides Gionis (KTH Royal Institute of Technology)
BibTeX Citation
@article{zhang_vldb26,
title = {{Efficient Banzhaf-Based Data Valuation for k-Nearest Neighbors Classification}},
author = {Zhang, Guangyi and Oettershagen, Lutz and Wang, Lixu and Gionis, Aristides},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {9},
pages = {1880--1892},
doi = {10.14778/3819518.3819521},
url = {https://doi.org/10.14778/3819518.3819521},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 894 | Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms | 2019 | VLDB | 0.00013212578 |
| 4,928 | Efficient Sampling Approaches to Shapley Value Approximation | 2023 | SIGMOD | 6.3502007e-05 |
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