论文标题
更少的是：呼吁专注于可解释机器学习的基因编程中的简单模型
Less is More: A Call to Focus on Simpler Models in Genetic Programming for Interpretable Machine Learning
论文作者
论文摘要
可解释性对于在高风险应用中对机器学习模型的安全和负责任的使用至关重要。到目前为止，进化计算（EC），尤其是遗传编程（GP）的形式，代表了发现可解释的机器学习（IML）模型的关键推动力。在这篇简短的论文中，我们认为，GP对IML的研究需要通过研究新型的搜索策略和重组方法来集中精力在低复杂模型的空间中进行搜索。此外，根据我们将研究带入临床实践的经验，我们认为研究应该努力设计更好的建模和追求可解释性的方法，以最终获得最有用的解决方案。
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.
