Distribution-Informed Adaptation for kNN Graph Construction

TL;DR

This paper introduces DaNNG, a distribution-aware adaptive kNN graph construction method that improves classification accuracy by tailoring k-values based on data distribution, especially around ambiguous decision boundary samples.

Contribution

The paper proposes a novel distribution-informed adaptive kNN graph construction method that enhances performance on ambiguous samples and improves overall accuracy.

Findings

DaNNG outperforms state-of-the-art algorithms on benchmark datasets.

It significantly improves classification accuracy for ambiguous boundary samples.

Demonstrates robustness across diverse real-world scenarios.

Abstract

Graph-based kNN algorithms have garnered widespread popularity for machine learning tasks due to their simplicity and effectiveness. However, as factual data often inherit complex distributions, the conventional kNN graph's reliance on a unified k-value can hinder its performance. A crucial factor behind this challenge is the presence of ambiguous samples along decision boundaries that are inevitably more prone to incorrect classifications. To address the situation, we propose the Distribution-Informed adaptive kNN Graph (DaNNG), which combines adaptive kNN with distribution-aware graph construction. By incorporating an approximation of the distribution with customized k-adaption criteria, DaNNG can significantly improve performance on ambiguous samples, and hence enhance overall accuracy and generalization capability. Through rigorous evaluations on diverse benchmark datasets, DaNNG outperforms state-of-the-art algorithms, showcasing its adaptability and efficacy across various real-world scenarios.