On Explaining Machine Learning Models by Evolving Crucial and Compact Features

4 July 2019

Papers citing "On Explaining Machine Learning Models by Evolving Crucial and Compact Features"

9 / 9 papers shown

Title
Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems Ryan Zhou Jaume Bacardit Alexander Brownlee Stefano Cagnoni Martin Fyvie Giovanni Iacca John Mccall Niki van Stein David Walker Ting-Kuei Hu 39 0 0 12 Jun 2024
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data Mafalda Malafaia Thalea Schlender Peter A. N. Bosman Tanja Alderliesten 29 0 0 19 Feb 2024
Evolutionary approaches to explainable machine learning Ryan Zhou Ting-Kuei Hu 35 7 0 23 Jun 2023
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning M. Virgolin Eric Medvet Tanja Alderliesten Peter A. N. Bosman 29 6 0 05 Apr 2022
On genetic programming representations and fitness functions for interpretable dimensionality reduction Thomas Uriot M. Virgolin Tanja Alderliesten Peter A. N. Bosman 4 9 0 01 Mar 2022
Model Learning with Personalized Interpretability Estimation (ML-PIE) M. Virgolin A. D. Lorenzo Francesca Randone Eric Medvet M. Wahde 24 29 0 13 Apr 2021
Genetic Programming is Naturally Suited to Evolve Bagging Ensembles M. Virgolin 20 0 0 13 Sep 2020
Learning a Formula of Interpretability to Learn Interpretable Formulas M. Virgolin A. D. Lorenzo Eric Medvet Francesca Randone 22 33 0 23 Apr 2020
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R Marvin N. Wright A. Ziegler 93 2,732 0 18 Aug 2015