# Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees

## TL;DR

This paper introduces three novel methods for binary classification that combine one-class classifiers with non-parametric models, specifically N-ary Trees and Minimum Spanning Trees, to improve performance on complex datasets.

## Contribution

It proposes new approaches leveraging combined one-class classifiers with MST and N-ary Trees for binary classification, addressing multi-modal distributions and classifier inconsistencies.

## Key findings

- Methods are feasible and perform comparably to state-of-the-art algorithms.
- Approaches effectively handle multi-modal class distributions.
- Combining classifiers improves robustness in complex classification tasks.

## Abstract

One-class classifiers are trained with target class only samples. Intuitively, their conservative modelling of the class description may benefit classical classification tasks where classes are difficult to separate due to overlapping and data imbalance. In this work, three methods are proposed which leverage on the combination of one-class classifiers based on non-parametric models, N-ary Trees and Minimum Spanning Trees class descriptors (MST-CD), to tackle binary classification problems. The methods deal with the inconsistencies arising from combining multiple classifiers and with spurious connections that MST-CD creates in multi-modal class distributions. As shown by our tests on several datasets, the proposed approach is feasible and comparable with state-of-the-art algorithms.