论文标题
暹罗元学习和算法选择“算法 - 性能角色” [提案]
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
论文作者
论文摘要
自动化算法选择通常优于单一学习者。通过元学习算法选择算法的关键通常是（元）特征，尽管有时没有提供足够的信息来有效训练元学习者。我们提出了一种用于自动化算法选择的暹罗神经网络体系结构，该算法选择更多地侧重于“相同性能”实例，而不是元功能。我们的工作包括一种新颖的性能指标和选择培训样本的方法。我们进一步介绍了“算法性能角色”的概念，该算法描述了单个算法执行的实例。 “相似的执行算法”作为选择培训样本的基础真理的概念是新颖的，并且正如我们所相信的那样具有巨大的潜力。在此提案中，我们详细概述了我们的想法，并提供了第一个证据，表明我们提出的指标更适合培训样本选择，以标准性能指标（例如绝对错误）。
Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do not provide enough information to train a meta-learner effectively. We propose a Siamese Neural Network architecture for automated algorithm selection that focuses more on 'alike performing' instances than meta-features. Our work includes a novel performance metric and method for selecting training samples. We introduce further the concept of 'Algorithm Performance Personas' that describe instances for which the single algorithms perform alike. The concept of 'alike performing algorithms' as ground truth for selecting training samples is novel and provides a huge potential as we believe. In this proposal, we outline our ideas in detail and provide the first evidence that our proposed metric is better suitable for training sample selection that standard performance metrics such as absolute errors.
