# MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric

arXiv: https://arxiv.org/abs/1809.06846 · HTML: https://mlsanity.com/paper/1809.06846/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Diverse, Global and Amortised Counterfactual Explanations for Uncertainty Estimates](https://mlsanity.com/paper/2112.02646/) (2021)
- [δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates](https://mlsanity.com/paper/2104.06323/) (2021)
- [An Improved Nearest Neighbour Classifier](https://mlsanity.com/paper/2204.13141/) (2022)

## Abstract (arXiv)

The MNIST dataset of the handwritten digits is known as one of the commonly used datasets for machine learning and computer vision research. We aim to study a widely applicable classification problem and apply a simple yet efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate the performance of the K-nearest neighbor classification algorithm on the MNIST dataset where the L2 Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradation due to slight spatial misalignments. The accuracy metric and confusion matrices are used as the performance indicators to compare the performance of the baseline algorithm versus the enhanced sliding window method and results show significant improvement using this proposed method.