A Preliminary Counterfactual Explanation Method for Genetic Programming-Evolved Rules: A Case Study on Uncertain Capacitated Arc Routing Problem
Author:
Affiliation:
1. Zhejiang Geely Holding Group, Hangzhou, China
2. Victoria University of Wellington, Wellington, New Zealand
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3638530.3654192
Reference15 articles.
1. Leonardo Augusto Ferreira, Frederico Gadelha Guimarães, and Rodrigo Silva. 2020. Applying Genetic Programming to Improve Interpretability in Machine Learning Models. arXiv preprint arXiv:2005.09512 (2020).
2. Generating interpretable fuzzy controllers using particle swarm optimization and genetic programming
3. Interpretable policies for reinforcement learning by genetic programming
4. Amir-Hossein Karimi, Gilles Barthe, Borja Balle, and Isabel Valera. 2020. Model-agnostic counterfactual explanations for consequential decisions. In International Conference on Artificial Intelligence and Statistics. PMLR, 895--905.
5. The Mythos of Model Interpretability
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