# The Resistance to Label Noise in K-NN and DNN Depends on its Concentration

arXiv: https://arxiv.org/abs/1803.11410 · HTML: https://mlsanity.com/paper/1803.11410/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2018.

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

- [Deep learning-based ecological analysis of camera trap images is impacted by training data quality and quantity](https://mlsanity.com/paper/2408.14348/) (2024)

## Abstract (arXiv)

We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's prediction for a given test example depends on the labels of the training examples in its local neighborhood. This motivates us to derive a realizable analytic expression that approximates the multi-class K-NN classification error in the presence of label noise, which is of independent importance. We then suggest that the expression for K-NN may serve as a first-order approximation for the DNN error. Finally, we demonstrate empirically the proximity of the developed expression to the observed performance of K-NN and DNN classifiers. Our result may explain the already observed surprising resistance of DNN to some types of label noise. It also characterizes an important factor of it showing that the more concentrated the noise the greater is the degradation in performance.