# Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors

arXiv: https://arxiv.org/abs/2312.01991 · HTML: https://mlsanity.com/paper/2312.01991/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 4 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Consistent Individualized Feature Attribution for Tree Ensembles](https://mlsanity.com/paper/1802.03888/) (2018)
- [Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach](https://mlsanity.com/paper/1409.0919/) (2014)
- [A Weighted Mutual k-Nearest Neighbour for Classification Mining](https://mlsanity.com/paper/2005.08640/) (2020)

## Abstract (arXiv)

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.