# Relationship between Variants of One-Class Nearest Neighbours and Creating their Accurate Ensembles

arXiv: https://arxiv.org/abs/1604.01686 · HTML: https://mlsanity.com/paper/1604.01686/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2016.

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

- [K-nearest neighbour and dynamic time warping for online signature verification](https://mlsanity.com/paper/2111.14438/) (2021)

## Abstract (arXiv)

In one-class classification problems, only the data for the target class is available, whereas the data for the non-target class may be completely absent. In this paper, we study one-class nearest neighbour (OCNN) classifiers and their different variants. We present a theoretical analysis to show the relationships among different variants of OCNN that may use different neighbours or thresholds to identify unseen examples of the non-target class. We also present a method based on inter-quartile range for optimising parameters used in OCNN in the absence of non-target data during training. Then, we propose two ensemble approaches based on random subspace and random projection methods to create accurate OCNN ensembles. We tested the proposed methods on 15 benchmark and real world domain-specific datasets and show that random-projection ensembles of OCNN perform best.