Meta learning is an advanced field of artificial intelligence where automatic learning algorithms are applied to acquire learning experience for a set of learning algorithms to improve learning performance. One of popular meta learning methodologies is based on cross validation, especially for selection processes among different machine learning models. However, the challenge is that it is very time-consuming to do cross validation among models in large data sets, especially in financial big data with high noise. This article proposes two asymptotic meta learning algorithms (AML-Lin and AML-Xiang), which are ordinal optimization algorithms for meta learning based on cross validation. The numerical experiments and real-world cases are conducted to illustrate its efficiency in cross validation of models in different scenarios, especially for financial data. The method proposed in this article has significant improvement by comparing with those ones in existing algorithms OCBA and IAML (e.g., see the work done by Chen et al. and Lin et al.),8 ,9 and it is new in dealing with financial data.

中文翻译：

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用于金融数据模型交叉验证的渐近元学习

元学习是人工智能的一个高级领域，它应用自动学习算法来获取一组学习算法的学习经验，以提高学习性能。一种流行的元学习方法是基于交叉验证，特别是对于不同机器学习模型之间的选择过程。然而，挑战在于在大数据集中进行模型之间的交叉验证非常耗时，尤其是在具有高噪声的金融大数据中。本文提出了两种渐近元学习算法（AML-Lin 和 AML-Xiang），它们是基于交叉验证的元学习的序数优化算法。进行了数值实验和实际案例，以说明其在不同场景下模型交叉验证的效率，尤其是财务数据。本文提出的方法与现有算法 OCBA 和 IAML 中的方法相比有显着的改进（例如，参见 Chen 等人和 Lin 等人所做的工作），8 ,9 并且它是处理金融问题的新方法。数据。