# Building Ensembles of Adaptive Nested Dichotomies with Random-Pair Selection

arXiv: https://arxiv.org/abs/1604.01854 · HTML: https://mlsanity.com/paper/1604.01854/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2016.

## Built on by (most cited first, 2 of 2)

- [Information-theoretic Classification Accuracy: A Criterion that Guides Data-driven Combination of Ambiguous Outcome Labels in Multi-class Classification](https://mlsanity.com/paper/2109.00582/) (2021)
- [ndscape: a structural map of the nested dichotomy space](https://mlsanity.com/paper/2610.18050/) (2026)

## Abstract (arXiv)

A system of nested dichotomies is a method of decomposing a multi-class problem into a collection of binary problems. Such a system recursively splits the set of classes into two subsets, and trains a binary classifier to distinguish between each subset. Even though ensembles of nested dichotomies with random structure have been shown to perform well in practice, using a more sophisticated class subset selection method can be used to improve classification accuracy. We investigate an approach to this problem called random-pair selection, and evaluate its effectiveness compared to other published methods of subset selection. We show that our method outperforms other methods in many cases when forming ensembles of nested dichotomies, and is at least on par in all other cases.