# A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules

arXiv: https://arxiv.org/abs/2209.07575 · HTML: https://mlsanity.com/paper/2209.07575/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 23 Aug 2022.

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

- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [MurTree: Optimal Classification Trees via Dynamic Programming and Search](https://mlsanity.com/paper/2007.12652/) (2020)
- [On Explaining Decision Trees](https://mlsanity.com/paper/2010.11034/) (2020)
- [On Tackling Explanation Redundancy in Decision Trees](https://mlsanity.com/paper/2205.09971/) (2022)

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

- [EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurations](https://mlsanity.com/paper/2402.00491/) (2024)

## Abstract (arXiv)

Our goal in this paper is to automatically extract a set of decision rules (rule set) that best explains a classification data set. First, a large set of decision rules is extracted from a set of decision trees trained on the data set. The rule set should be concise, accurate, have a maximum coverage and minimum number of inconsistencies. This problem can be formalized as a modified version of the weighted budgeted maximum coverage problem, known to be NP-hard. To solve the combinatorial optimization problem efficiently, we introduce a nested genetic algorithm which we then use to derive explanations for ten public data sets.