# Separating Rule Discovery and Global Solution Composition in a Learning Classifier System

arXiv: https://arxiv.org/abs/2202.01677 · HTML: https://mlsanity.com/paper/2202.01677/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis](https://mlsanity.com/paper/1606.04316/) (2016)
- [Statistical comparison of classifiers through Bayesian hierarchical modelling](https://mlsanity.com/paper/1609.08905/) (2016)
- [SupRB: A Supervised Rule-based Learning System for Continuous Problems](https://mlsanity.com/paper/2002.10295/) (2020)

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

- [Investigating the Impact of Independent Rule Fitnesses in a Learning Classifier System](https://mlsanity.com/paper/2207.05582/) (2022)

## Abstract (arXiv)

While utilization of digital agents to support crucial decision making is increasing, trust in suggestions made by these agents is hard to achieve. However, it is essential to profit from their application, resulting in a need for explanations for both the decision making process and the model. For many systems, such as common black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. We propose a rule-based learning system specifically conceptualised and, thus, especially suited for these scenarios. Its models are inherently transparent and easily interpretable by design. One key innovation of our system is that the rules' conditions and which rules compose a problem's solution are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability.