# PL-kNN: A Parameterless Nearest Neighbors Classifier

arXiv: https://arxiv.org/abs/2209.12647 · HTML: https://mlsanity.com/paper/2209.12647/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [How to reduce the number of rating scale items without predictability loss?](https://mlsanity.com/paper/1505.01886/) (2015)
- [A Metaheuristic-Driven Approach to Fine-Tune Deep Boltzmann Machines](https://mlsanity.com/paper/2101.05795/) (2021)

## Built on by (most cited first, 2 of 2)

- [Adaptive k-nearest neighbor classifier based on the local estimation of the shape operator](https://mlsanity.com/paper/2409.05084/) (2024)
- [Distribution-Informed Adaptation for kNN Graph Construction](https://mlsanity.com/paper/2308.02442/) (2023)

## Abstract (arXiv)

Demands for minimum parameter setup in machine learning models are desirable to avoid time-consuming optimization processes. The k-Nearest Neighbors is one of the most effective and straightforward models employed in numerous problems. Despite its well-known performance, it requires the value of k for specific data distribution, thus demanding expensive computational efforts. This paper proposes a k-Nearest Neighbors classifier that bypasses the need to define the value of k. The model computes the k value adaptively considering the data distribution of the training set. We compared the proposed model against the standard k-Nearest Neighbors classifier and two parameterless versions from the literature. Experiments over 11 public datasets confirm the robustness of the proposed approach, for the obtained results were similar or even better than its counterpart versions.