# An Incremental MaxSAT-based Model to Learn Interpretable and Balanced Classification Rules

arXiv: https://arxiv.org/abs/2403.16418 · HTML: https://mlsanity.com/paper/2403.16418/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 25 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI](https://mlsanity.com/paper/1907.07374/) (2019)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [Computing Optimal Decision Sets with SAT](https://mlsanity.com/paper/2007.15140/) (2020)
- [MLIC: A MaxSAT-Based framework for learning interpretable classification rules](https://mlsanity.com/paper/1812.01843/) (2018)
- [IMLI: An Incremental Framework for MaxSAT-Based Learning of Interpretable Classification Rules](https://mlsanity.com/paper/2001.01891/) (2020)
- [LIBRE: Learning Interpretable Boolean Rule Ensembles](https://mlsanity.com/paper/1911.06537/) (2019)

## Abstract (arXiv)

The increasing advancements in the field of machine learning have led to the development of numerous applications that effectively address a wide range of problems with accurate predictions. However, in certain cases, accuracy alone may not be sufficient. Many real-world problems also demand explanations and interpretability behind the predictions. One of the most popular interpretable models that are classification rules. This work aims to propose an incremental model for learning interpretable and balanced rules based on MaxSAT, called IMLIB. This new model was based on two other approaches, one based on SAT and the other on MaxSAT. The one based on SAT limits the size of each generated rule, making it possible to balance them. We suggest that such a set of rules seem more natural to be understood compared to a mixture of large and small rules. The approach based on MaxSAT, called IMLI, presents a technique to increase performance that involves learning a set of rules by incrementally applying the model in a dataset. Finally, IMLIB and IMLI are compared using diverse databases. IMLIB obtained results comparable to IMLI in terms of accuracy, generating more balanced rules with smaller sizes.