A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules
Neural and Evolutionary Computing
2022-09-19 v1 Artificial Intelligence
Machine Learning
Abstract
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.
Cite
@article{arxiv.2209.07575,
title = {A Nested Genetic Algorithm for Explaining Classification Data Sets with Decision Rules},
author = {Paul-Amaury Matt and Rosina Ziegler and Danilo Brajovic and Marco Roth and Marco F. Huber},
journal= {arXiv preprint arXiv:2209.07575},
year = {2022}
}