# Concise and interpretable multi-label rule sets

arXiv: https://arxiv.org/abs/2210.01533 · HTML: https://mlsanity.com/paper/2210.01533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 4 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [A note on the triangle inequality for the Jaccard distance](https://mlsanity.com/paper/1612.02696/) (2016)
- [Diverse Rule Sets](https://mlsanity.com/paper/2006.09890/) (2020)
- [Learning Gradient Boosted Multi-label Classification Rules](https://mlsanity.com/paper/2006.13346/) (2020)
- [Better Short than Greedy: Interpretable Models through Optimal Rule Boosting](https://mlsanity.com/paper/2101.08380/) (2021)
- [Efficient Learning of Interpretable Classification Rules](https://mlsanity.com/paper/2205.06936/) (2022)
- [Mining Maximal Cliques from an Uncertain Graph](https://mlsanity.com/paper/1310.6780/) (2013)
- [Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning](https://mlsanity.com/paper/1908.06874/) (2019)
- [Gradient-based Label Binning in Multi-label Classification](https://mlsanity.com/paper/2106.11690/) (2021)

## Built on by (most cited first, 1 of 1)

- [Scalable Rule Lists Learning with Sampling](https://mlsanity.com/paper/2406.12803/) (2024)

## Abstract (arXiv)

Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a multi-label classifier that can be represented as a concise set of simple "if-then" rules, and thus, it offers better interpretability compared to black-box models. Notably, our method is able to find a small set of relevant patterns that lead to accurate multi-label classification, while existing rule-based classifiers are myopic and wasteful in searching rules,requiring a large number of rules to achieve high accuracy. In particular, we formulate the problem of choosing multi-label rules to maximize a target function, which considers not only discrimination ability with respect to labels, but also diversity. Accounting for diversity helps to avoid redundancy, and thus, to control the number of rules in the solution set. To tackle the said maximization problem we propose a 2-approximation algorithm, which relies on a novel technique to sample high-quality rules. In addition to our theoretical analysis, we provide a thorough experimental evaluation, which indicates that our approach offers a trade-off between predictive performance and interpretability that is unmatched in previous work.