# Hierarchical confusion matrix for classification performance evaluation

arXiv: https://arxiv.org/abs/2306.09461 · HTML: https://mlsanity.com/paper/2306.09461/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 1 of 1)

- [Evaluation Measures for Hierarchical Classification: a unified view and novel approaches](https://mlsanity.com/paper/1306.6802/) (2013)

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

- [Taxonomy-Aware Evaluation of Vision-Language Models](https://mlsanity.com/paper/2504.05457/) (2025)
- [RAM: Towards an Ever-Improving Memory System by Learning from Communications](https://mlsanity.com/paper/2404.12045/) (2024)
- [H-DDx: A Hierarchical Evaluation Framework for Differential Diagnosis](https://mlsanity.com/paper/2510.03700/) (2025)
- [Mitigating Context Bias in Domain Adaptation for Object Detection using Mask Pooling](https://mlsanity.com/paper/2505.18446/) (2025)
- [hYOLO Model: Enhancing Object Classification with Hierarchical Context in YOLOv8](https://mlsanity.com/paper/2510.23278/) (2025)
- [Climbing the label tree: Hierarchy-preserving contrastive learning for medical imaging](https://mlsanity.com/paper/2511.03771/) (2025)

## Abstract (arXiv)

In this work we propose a novel concept of a hierarchical confusion matrix, opening the door for popular confusion matrix based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. We develop the concept to a generalized form and prove its applicability to all types of hierarchical classification problems including directed acyclic graphs, multi path labelling, and non mandatory leaf node prediction. Finally, we use measures based on the novel confusion matrix to evaluate models within a benchmark for three real world hierarchical classification applications and compare the results to established evaluation measures. The results outline the reasonability of this approach and its usefulness to evaluate hierarchical classification problems. The implementation of hierarchical confusion matrix is available on GitHub.