Title: Assessing the Generalizability of a Performance Predictive Model Show Chinese title

Title: 评估性能预测模型的泛化能力

Abstract: A key component of automated algorithm selection and configuration, which in most cases are performed using supervised machine learning (ML) methods is a good-performing predictive model. The predictive model uses the feature representation of a set of problem instances as input data and predicts the algorithm performance achieved on them. Common machine learning models struggle to make predictions for instances with feature representations not covered by the training data, resulting in poor generalization to unseen problems. In this study, we propose a workflow to estimate the generalizability of a predictive model for algorithm performance, trained on one benchmark suite to another. The workflow has been tested by training predictive models across benchmark suites and the results show that generalizability patterns in the landscape feature space are reflected in the performance space. Show Chinese abstract

Abstract: 自动化算法选择和配置的关键组成部分，通常在大多数情况下是使用监督机器学习（ML）方法进行的，是一个表现良好的预测模型。 预测模型使用一组问题实例的特征表示作为输入数据，并预测在这些实例上实现的算法性能。 常见的机器学习模型难以对训练数据未覆盖的特征表示实例进行预测，导致对未见过的问题泛化能力较差。 在本研究中，我们提出了一种工作流程，用于估计在一套基准测试中训练的算法性能预测模型在另一套基准测试中的泛化能力。 该工作流程通过在基准测试套件之间训练预测模型进行了测试，结果表明，在景观特征空间中的泛化能力模式反映在性能空间中。