# Robust Optimal Classification Trees under Noisy Labels

arXiv: https://arxiv.org/abs/2012.08560 · HTML: https://mlsanity.com/paper/2012.08560/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2020.

## Built on by (most cited first, 6 of 6)

- [Margin Optimal Classification Trees](https://mlsanity.com/paper/2210.10567/) (2022)
- [Multiclass Optimal Classification Trees with SVM-splits](https://mlsanity.com/paper/2111.08674/) (2021)
- [An improved column-generation-based matheuristic for learning classification trees](https://mlsanity.com/paper/2308.11477/) (2023)
- [A Mathematical Programming Approach to Optimal Classification Forests](https://mlsanity.com/paper/2211.10502/) (2022)
- [A Mathematical Optimization Approach to Multisphere Support Vector Data Description](https://mlsanity.com/paper/2507.11106/) (2025)
- [A Unified Optimization Framework for Multiclass Classification with Structured Hyperplane Arrangements](https://mlsanity.com/paper/2510.05047/) (2025)

## Abstract (arXiv)

In this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the construction of the tree trying to detect the label noise. Both features are considered and integrated together to design the resulting Optimal Classification Tree. We present a Mixed Integer Non Linear Programming formulation for the problem, suitable to be solved using any of the available off-the-shelf solvers. The model is analyzed and tested on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of our approach.