# Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge

arXiv: https://arxiv.org/abs/2407.15192 · HTML: https://mlsanity.com/paper/2407.15192/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 21 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification](https://mlsanity.com/paper/2107.07511/) (2021)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Uncertainty Sets for Image Classifiers using Conformal Prediction](https://mlsanity.com/paper/2009.14193/) (2020)
- [A Semantic Loss Function for Deep Learning with Symbolic Knowledge](https://mlsanity.com/paper/1711.11157/) (2017)
- [Neurosymbolic AI: The 3rd Wave](https://mlsanity.com/paper/2012.05876/) (2020)
- [Logic Tensor Networks](https://mlsanity.com/paper/2012.13635/) (2020)
- [Semantic Probabilistic Layers for Neuro-Symbolic Learning](https://mlsanity.com/paper/2206.00426/) (2022)
- [Failing to Learn: Autonomously Identifying Perception Failures for Self-driving Cars](https://mlsanity.com/paper/1707.00051/) (2017)
- [Introspective Perception: Learning to Predict Failures in Vision Systems](https://mlsanity.com/paper/1607.08665/) (2016)
- [Rule-Based Error Detection and Correction to Operationalize Movement Trajectory Classification](https://mlsanity.com/paper/2308.14250/) (2023)
- [Recent Advances in Hierarchical Multi-label Text Classification: A Survey](https://mlsanity.com/paper/2307.16265/) (2023)

## Built on by (most cited first, 7 of 7)

- [Metal Price Spike Prediction via a Neurosymbolic Ensemble Approach](https://mlsanity.com/paper/2410.12785/) (2024)
- [Hierarchy-Consistent Learning and Adaptive Loss Balancing for Hierarchical Multi-Label Classification](https://mlsanity.com/paper/2508.13452/) (2025)
- [Probabilistic Foundations for Metacognition via Hybrid-AI](https://mlsanity.com/paper/2502.05398/) (2025)
- [Machine Learning Model Integration with Open World Temporal Logic for Process Automation](https://mlsanity.com/paper/2506.17776/) (2025)
- [Error Detection and Correction for Interpretable Mathematics in Large Language Models](https://mlsanity.com/paper/2508.03500/) (2025)
- [Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI](https://mlsanity.com/paper/2605.15567/) (2026)
- [Adversarially Robust Abductive Fusion of Pre-trained Transformer-based Perception Models](https://mlsanity.com/paper/2608.04190/) (2026)

## Abstract (arXiv)

Recent advances in Hierarchical Multi-label Classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during training. However, such work assumes the existence of such constraints a-priori. In this paper, we relax this strong assumption and present an approach based on Error Detection Rules (EDR) that allow for learning explainable rules about the failure modes of machine learning models. We show that these rules are not only effective in detecting when a machine learning classifier has made an error but also can be leveraged as constraints for HMC, thereby allowing the recovery of explainable constraints even if they are not provided. We show that our approach is effective in detecting machine learning errors and recovering constraints, is noise tolerant, and can function as a source of knowledge for neurosymbolic models on multiple datasets, including a newly introduced military vehicle recognition dataset.