# Enhanced Nearest Neighbor Classification for Crowdsourcing

arXiv: https://arxiv.org/abs/2203.00781 · HTML: https://mlsanity.com/paper/2203.00781/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 45 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Transfer Learning for Nonparametric Classification: Minimax Rate and Adaptive Classifier](https://mlsanity.com/paper/1906.02903/) (2019)
- [Rates of Convergence for Nearest Neighbor Classification](https://mlsanity.com/paper/1407.0067/) (2014)
- [An ensemble-based system for automatic screening of diabetic retinopathy](https://mlsanity.com/paper/1410.8576/) (2014)
- [Achieving the time of 1-NN, but the accuracy of k-NN](https://mlsanity.com/paper/1712.02369/) (2017)
- [Near-optimal sample compression for nearest neighbors](https://mlsanity.com/paper/1404.3368/) (2014)

## Abstract (arXiv)

In machine learning, crowdsourcing is an economical way to label a large amount of data. However, the noise in the produced labels may deteriorate the accuracy of any classification method applied to the labelled data. We propose an enhanced nearest neighbor classifier (ENN) to overcome this issue. Two algorithms are developed to estimate the worker quality (which is often unknown in practice): one is to construct the estimate based on the denoised worker labels by applying the kNN classifier to the expert data; the other is an iterative algorithm that works even without access to the expert data. Other than strong numerical evidence, our proposed methods are proven to achieve the same regret as its oracle version based on high-quality expert data. As a technical by-product, a lower bound on the sample size assigned to each worker to reach the optimal convergence rate of regret is derived.