Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning

arXiv (Cornell University)

Abstract

Interpretability can be critical for the safe and responsible use of machine learning models in high-stakes applications. So far, evolutionary computation (EC), in particular in the form of genetic programming (GP), represents a key enabler for the discovery of interpretable machine learning (IML) models. In this short paper, we argue that research in GP for IML needs to focus on searching in the space of low-complexity models, by investigating new kinds of search strategies and recombination methods. Moreover, based on our experience of bringing research into clinical practice, we believe that research should strive to design better ways of modeling and pursuing interpretability, for the obtained solutions to ultimately be most useful.

Authors 4

1. Marco Virgolin

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica , Amsterdam , The Netherlands

2. Eric Medvet

   University of Trieste

   Affiliation as printed

   University of Trieste , Trieste , Italy

3. Tanja Alderliesten Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center , Leiden , The Netherlands

4. Peter A. N. Bosman

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica , Amsterdam , The Netherlands