Twin Neural Network Improved k-Nearest Neighbor Regression

TL;DR

This paper introduces a novel neural network approach that predicts differences between targets to enhance k-nearest neighbor regression, demonstrating improved accuracy on small to medium datasets.

Contribution

It presents a twin neural network model trained on differences, combining neural networks and k-NN regression for better performance.

Findings

Outperforms traditional neural networks on regression tasks.

Achieves higher accuracy than standard k-NN regression.

Effective on small to medium-sized datasets.

Abstract

Twin neural network regression is trained to predict differences between regression targets rather than the targets themselves. A solution to the original regression problem can be obtained by ensembling predicted differences between the targets of an unknown data point and multiple known anchor data points. Choosing the anchors to be the nearest neighbors of the unknown data point leads to a neural network-based improvement of k-nearest neighbor regression. This algorithm is shown to outperform both neural networks and k-nearest neighbor regression on small to medium-sized data sets.