# Diverse Rule Sets

arXiv: https://arxiv.org/abs/2006.09890 · HTML: https://mlsanity.com/paper/2006.09890/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach](https://mlsanity.com/paper/2206.03718/) (2022)
- [Efficient Exploration of the Rashomon Set of Rule Set Models](https://mlsanity.com/paper/2406.03059/) (2024)
- [Better Short than Greedy: Interpretable Models through Optimal Rule Boosting](https://mlsanity.com/paper/2101.08380/) (2021)
- [Truly Unordered Probabilistic Rule Sets for Multi-class Classification](https://mlsanity.com/paper/2206.08804/) (2022)
- [Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0](https://mlsanity.com/paper/2412.10967/) (2024)
- [CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://mlsanity.com/paper/2407.01004/) (2024)
- [Concise and interpretable multi-label rule sets](https://mlsanity.com/paper/2210.01533/) (2022)
- [Probabilistic Truly Unordered Rule Sets](https://mlsanity.com/paper/2401.09918/) (2024)
- [Personalized Interpretable Classification](https://mlsanity.com/paper/2302.02528/) (2023)
- [On Finding Bi-objective Pareto-optimal Fraud Prevention Rule Sets for Fintech Applications](https://mlsanity.com/paper/2311.00964/) (2023)
- [Minimising changes to audit when updating decision trees](https://mlsanity.com/paper/2408.16321/) (2024)
- [Learning High Coverage Discriminative Parsimonious Rulesets](https://mlsanity.com/paper/2606.14156/) (2026)

## Abstract (arXiv)

While machine-learning models are flourishing and transforming many aspects of everyday life, the inability of humans to understand complex models poses difficulties for these models to be fully trusted and embraced. Thus, interpretability of models has been recognized as an equally important quality as their predictive power. In particular, rule-based systems are experiencing a renaissance owing to their intuitive if-then representation. However, simply being rule-based does not ensure interpretability. For example, overlapped rules spawn ambiguity and hinder interpretation. Here we propose a novel approach of inferring diverse rule sets, by optimizing small overlap among decision rules with a 2-approximation guarantee under the framework of Max-Sum diversification. We formulate the problem as maximizing a weighted sum of discriminative quality and diversity of a rule set. In order to overcome an exponential-size search space of association rules, we investigate several natural options for a small candidate set of high-quality rules, including frequent and accurate rules, and examine their hardness. Leveraging the special structure in our formulation, we then devise an efficient randomized algorithm, which samples rules that are highly discriminative and have small overlap. The proposed sampling algorithm analytically targets a distribution of rules that is tailored to our objective. We demonstrate the superior predictive power and interpretability of our model with a comprehensive empirical study against strong baselines.