论文标题
应用遗传编程以提高机器学习模型中的可解释性
Applying Genetic Programming to Improve Interpretability in Machine Learning Models
论文作者
论文摘要
可解释的人工智能（或XAI）已成为机器学习和深度学习领域的重要研究主题。在本文中，我们提出了一种基于遗传编程（GP）的方法，即遗传编程解释器（GPX），以解释AI系统计算的决策的问题。该方法生成位于感兴趣点附近的噪声集，该噪声集应解释其预测，并适合分析样本的局部解释模型。 GPX生成的树结构提供了可理解的分析，可能是非线性的象征性表达，反映了复杂模型的局部行为。我们考虑了三种可以将其视为复杂黑盒模型的机器学习技术：随机森林，深神经网络和支持向量机，用于二十个数据集，用于回归和分类问题。我们的结果表明，GPX能够比艺术的状态更准确地了解复杂模型。结果将提出的方法验证为一种新型方法，用于部署GP以提高可解释性。
Explainable Artificial Intelligence (or xAI) has become an important research topic in the fields of Machine Learning and Deep Learning. In this paper, we propose a Genetic Programming (GP) based approach, named Genetic Programming Explainer (GPX), to the problem of explaining decisions computed by AI systems. The method generates a noise set located in the neighborhood of the point of interest, whose prediction should be explained, and fits a local explanation model for the analyzed sample. The tree structure generated by GPX provides a comprehensible analytical, possibly non-linear, symbolic expression which reflects the local behavior of the complex model. We considered three machine learning techniques that can be recognized as complex black-box models: Random Forest, Deep Neural Network and Support Vector Machine in twenty data sets for regression and classifications problems. Our results indicate that the GPX is able to produce more accurate understanding of complex models than the state of the art. The results validate the proposed approach as a novel way to deploy GP to improve interpretability.
