# Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach

arXiv: https://arxiv.org/abs/1409.0919 · HTML: https://mlsanity.com/paper/1409.0919/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2014.

## Built on by (most cited first, 7 of 7)

- [Sampling a Near Neighbor in High Dimensions -- Who is the Fairest of Them All?](https://mlsanity.com/paper/2101.10905/) (2021)
- [Flexible K Nearest Neighbors Classifier: Derivation and Application for Ion-mobility Spectrometry-based Indoor Localization](https://mlsanity.com/paper/2304.10151/) (2023)
- [Beyond MMD: Evaluating Graph Generative Models with Geometric Deep Learning](https://mlsanity.com/paper/2512.14241/) (2025)
- [Shapley-Based Data Valuation with Mutual Information: A Key to Modified K-Nearest Neighbors](https://mlsanity.com/paper/2312.01991/) (2023)
- [Classification of retail products: From probabilistic ranking to neural networks](https://mlsanity.com/paper/2312.07482/) (2023)
- [Labeling subtypes in a Parkinson's Cohort using Multifeatures in MRI -- Integrating Grey and White Matter Information](https://mlsanity.com/paper/2403.17332/) (2024)
- [ProtoGuide: Prototype-Driven Guidance for Class-Conditional Graph Generation](https://mlsanity.com/paper/2609.15239/) (2026)

## Abstract (arXiv)

This paper presents a new solution for choosing the K parameter in the k-nearest neighbor (KNN) algorithm, the solution depending on the idea of ensemble learning, in which a weak KNN classifier is used each time with a different K, starting from one to the square root of the size of the training set. The results of the weak classifiers are combined using the weighted sum rule. The proposed solution was tested and compared to other solutions using a group of experiments in real life problems. The experimental results show that the proposed classifier outperforms the traditional KNN classifier that uses a different number of neighbors, is competitive with other classifiers, and is a promising classifier with strong potential for a wide range of applications.