# Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization

arXiv: https://arxiv.org/abs/2304.10151 · HTML: https://mlsanity.com/paper/2304.10151/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 3 of 3)

- [The N-Tuple Bandit Evolutionary Algorithm for Game Agent Optimisation](https://mlsanity.com/paper/1802.05991/) (2018)
- [Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach](https://mlsanity.com/paper/1409.0919/) (2014)
- [On Enhancing The Performance Of Nearest Neighbour Classifiers Using Hassanat Distance Metric](https://mlsanity.com/paper/1501.00687/) (2015)

## Built on by (most cited first, 1 of 1)

- [(Weighted) Adaptive Radius Near Neighbor Search: Evaluation for WiFi Fingerprint-based Positioning](https://mlsanity.com/paper/2604.15940/) (2026)

## Abstract (arXiv)

The K Nearest Neighbors (KNN) classifier is widely used in many fields such as fingerprint-based localization or medicine. It determines the class membership of unlabelled sample based on the class memberships of the K labelled samples, the so-called nearest neighbors, that are closest to the unlabelled sample. The choice of K has been the topic of various studies and proposed KNN-variants. Yet no variant has been proven to outperform all other variants. In this paper a KNN-variant is discussed which ensures that the K nearest neighbors are indeed close to the unlabelled sample and finds K along the way. The algorithm is tested and compared to the standard KNN in theoretical scenarios and for indoor localization based on ion-mobility spectrometry fingerprints. It achieves a higher classification accuracy than the KNN in the tests, while having the same computational demand.